WO2013034420A1 - Condition monitoring of a system containing a feedback controller - Google Patents
Condition monitoring of a system containing a feedback controller Download PDFInfo
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- WO2013034420A1 WO2013034420A1 PCT/EP2012/066100 EP2012066100W WO2013034420A1 WO 2013034420 A1 WO2013034420 A1 WO 2013034420A1 EP 2012066100 W EP2012066100 W EP 2012066100W WO 2013034420 A1 WO2013034420 A1 WO 2013034420A1
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- asset
- condition monitoring
- feedback controller
- data
- condition
- Prior art date
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
- G05B19/0425—Safety, monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0235—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
Definitions
- the present invention relates to asset condition or health monitoring and more particularly to the monitoring of machines in service.
- Asset health monitoring commonly referred to as equipment health monitoring (EHM) is based around the premise of sensing a plurality of operational variables for an asset during use.
- the gathered data can be used to determine an operational state of the asset. Additionally the data can be processed to identify the current condition or health status of the equipment
- the output of the EHM system provides information to an operator which can be used to manage the operation of the equipment, for example by controlling the equipment in a manner which is sympathetic to the condition of the equipment or else by scheduling suitable repair or maintenance work.
- EHM electrostatic multi-reliable multi-reliable multi-reliable and low-power systems
- level of sophistication of an EHM system is often determined by the value or complexity of the asset. More particularly, sophisticated EHM systems are most often implemented where the cost of maintenance work to be carried out on the assets is relatively high. This therefore demands that maintenance schedules are optimised so that maintenance can be carried out effectively at appropriate intervals and with minimal disruption to the asset operation.
- Many asset control systems including gas turbine engine controllers, use feedback control as a means to achieve desired transient and steady-state performance.
- the benefits of such methods of control derive at least in part from the ability to cater for operational disturbances, which may be caused by any of the operating context, environment or equipment condition, including equipment faults.
- a feedback controller typically controls variables for a system to ensure the desired operation in spite of such disturbances.
- control system's compensation for the equipment's condition masks the changes the EHM system is intended to detect, thereby, potentially making the EHM systems less effective or less sensitive, which may delay, or completely mask the EHM systems ability to detect a problem in a timely manner.
- the present invention provides a condition monitoring system for an asset having a feedback controller, wherein the condition monitoring system is arranged to receive one or more internal signals of the feedback controller in order to determine an operational condition of the asset.
- a condition monitoring system for an asset comprising: a plurality of sensors for taking readings of operational variables of the asset; a feedback controller arranged to receive data indicative of the operational variables measured by said sensors and to issue control instructions for controlling operation of the asset; and a condition monitoring device arranged to receive one or more internal signals from the feedback controller so as to allow determination of an operational condition of the asset based there-upon.
- the feedback controller receives the data from the sensors as one or more inputs and processes the data in order to generate one or more outputs.
- the control instructions comprise at least one output.
- the feedback controller processes the received sensor data using one or more algorithms and thereby generates the one or more internal signals.
- control instructions are based upon, derived from or determined from the one or more internal signals.
- control instructions and internal signals are not the same.
- the internal signals may comprise a second or further output of the feedback controller.
- the feedback controller may be arranged to output said internal signal to the condition monitoring device automatically.
- the feedback controller may determine a difference between a current operational state of the asset and a desired operational state.
- the internal signal may comprise a parameter which is proportional to said difference.
- the internal signal may comprise a parameter which is based on a rate of change of said difference. Additionally or alternatively, the internal signal may comprise a parameter which is based on a duration of divergence of the current and desired operational states.
- the one or more internal signals comprise any or any combination of a proportional, differential and/or integral parameter value based upon said difference.
- the internal signal preferably relates to a parameter that displays persistence.
- the condition monitoring device is arranged to further receive the data indicative of the operational variables measured by said sensors. That data may be received directly from the sensors, typically over a network, or else may be received via the feedback controller.
- the condition monitoring device may determine an operating condition of the asset based upon a combination of the sensor data and the internal signals of the feedback controller.
- condition monitoring device can provide a greater degree of certainty in its interpretation of the asset condition by monitoring a further system variable, namely the internal signal(s) of the controller.
- a further system variable namely the internal signal(s) of the controller.
- components/devices of an asset can add significant cost and complexity to, not only the asset hardware itself, but also the assembly and maintenance thereof.
- the condition monitoring device may be arranged to receive the control instructions from the controller.
- the condition monitoring device may determine an operating condition of the asset based upon the internal signals of the feedback controller and any, or any combination, of the sensor data and/or the control instructions.
- the condition monitoring device may be included in the feedback controller, for example to improve cost and design simplicity.
- the condition monitoring device may comprise one or more processors which are separate to the control feedback processor(s) but housed within a common unit.
- the condition monitoring device and the control feedback device may exist on common processing means, for example being separated by a soft partition.
- a method of asset condition monitoring comprising: obtaining or receiving readings of asset operational variables from a plurality of sensors; obtaining or receiving an internal signal from an asset feedback controller; and, determining an operational state of the asset based on the combined readings and internal controller signal.
- the method may be undertaken continually or substantially continually during an instance, or period, of asset operation.
- the sampling interval of the data may be varied depending on the know operating state, and/or level of interest in the operating state. For instance, where precursors of a failure have been detected the sampling rate may be increased to provide greater sensitivity.
- condition monitoring methods are many, including, but not limited to, exceedance of thresholds to comparisons of "actual" condition monitoring signals with model based predictions.
- the method may output information or instructions derived from the determination of asset operational state, such as asset operation instructions , asset condition information and/or an asset maintenance instruction.
- a data carrier comprising computer readable instructions for controlling the operation of one or more processors to perform the method of the second aspect.
- a condition monitoring system for an asset having a feedback controller, the condition monitoring system comprising: a plurality of sensors for taking readings of operational variables of the asset; a condition monitoring device arranged to receive data indicative of the operational variables measures by said sensors, wherein the condition monitoring device further receives one or more internal signals from the feedback controller so as to allow determination of an operational condition of the asset based upon both the sensor readings and also the internal signal from the feedback controller.
- Any reference made herein to 'an asset' or 'equipment' may comprise a reference to a sub-assembly or component thereof.
- Figure 1 shows a schematic of a condition monitoring system according to the general principles of the present invention
- Figure 2 shows a flow diagram of exemplary control signals of the controller according to one embodiment of the invention
- Figure 3 is an exemplary plot of a measured variable against time for a machine
- Figure 4 shows an exemplary system of the present invention for gas turbine engines
- FIG. 5 is a schematic showing further details of the flow of data in the system of Figure 4.
- the present invention derives in general from the realisation by the inventor that the processing of data by the controller of an asset and the associated data signals used in the determination of control steps for the asset can be used to gain a greater insight into the health of the asset for condition monitoring purposes.
- An asset as referred to in the description below typically refers to a machine or a number of machines, which are inter-reliant for correct operation thereof.
- Computer control systems are used conventionally to operate machinery according to a control strategy.
- Simple control strategies may be used to control devices having a single, or relatively few, degrees of freedom such as valves, pistons, simple rotating drives and the like.
- more complicated control strategies are put in place where a machine or system has a number of interdependent sub-assemblies or components, each of which having a number of control inputs and outputs.
- controllers applying such control strategies typically receive operation data from, and have control over, a number of different sub-assemblies or components of the asset.
- FIG. 1 there is shown a control system 1 for a generic asset 2, which in this example is shown as a plant.
- the inputs may be, for example, materials, energy and/or operation demands and the outputs may comprise any, or any combination, of products, energy, waste materials or the like.
- a number of sensors 5 sense different operational variables, or the same operational variables at different locations, and the sensor readings are fed to an asset controller 6 which runs a series, or nest, of control loop programs in its software in order to generate suitable control signals for operation of the asset 2 to produce the desired output 4.
- internal control signals are generated as will be described by way of example with reference to Figure 3.
- Those internal signals are passed to an asset monitoring unit 7, which in this example may be referred to as a change detection element or unit, to be monitored.
- the change detection element 7 compares data it receives pertaining to an operational state of the asset and compares it to a model which defines a normal or acceptable operational state of the asset. Whilst such functionality represents one implementation for a system according the invention there are other ways of detecting or assessing changes in operational condition, such as for example by comparison of received data with a fault model in addition to, or instead of, a normal model. Any such a model will typically be stored at the change detection element or else be accessible thereby and comprises data pertaining to acceptable or threshold value ranges for the sensor readings and internal signals of the feedback controller.
- the change detection element 7, in different embodiments of the invention, may be provided as a further processing means within the same hardware as the controller 6 or else may be located remotely from the controller and in data communication therewith.
- the change detection element may be co-located with the controller 6; located elsewhere on the same asset and connected therewith over a local network; or else remotely located and in communication with the controller over a wider network.
- PID controller 6 applies a control feedback loop and are used to control a change in a machine from its current state to a currently desired, or reference, state.
- the controller 6 receives a desired reference condition or state and compares it to a current measured state, which is based on the data received from sensors 5, indicative of measurements of operational variables for the asset.
- the controller then aims to rectify the difference between the current and desired states (i.e. the control error) by instructing one or more changes in the operation of the asset 2. This is otherwise described as applying control effort.
- the control effort is controlled based on a combination of: a value proportional to the difference between the current state and the desired state (P), the rate of change between the two states (differential, D) and the duration of the divergence between the current and reference states (integral, I).
- the P, I and D signal elements may be weighted as part of the summation process in order to achieve suitable control of the machine. It will be appreciated that for some types of machine and/or control scenarios, only a simple P signal will be required, whereas in other situations a combination of two or more signals may be needed in order to achieve an optimal or stable control strategy.
- the control output from the controller 6 is then fed to the asset 2 and the resulting change in operation is measured and input to the start of the process as a feedback loop. Accordingly the control loop is repeated based on the changed state (or the new current state) of the asset.
- the control feedback loop typically operates
- a controller Whilst in a conventional system the controller outputs only control instructions, a controller performs a number of internal or intermediate steps or loops in order to arrive at those instructions. Accordingly the present invention seeks to access the internal or intermediate signals of the controller that contribute towards the final set of control instructions in order to glean useful EHM data there-from. Whilst the current example refers to those internal signals as P, I or D signal elements, it will be appreciated that a controller may use any of a number of different control strategies and may generate or handle a multitude of different internal parameter values. The term 'internal signals' is intended to encompass any such data that may otherwise not be available in a control signal output by the feedback controller. For example, a controller may operate a number of different loops depending on the operational context of an asset (e.g.
- FIG 3 an example is shown of a plot of displacement 's' against time 't' for an actuator, such as a hydraulic actuator.
- the dashed line represents a desired hydraulic actuation of a piston within a cylinder, whereas the solid line represents an actual plot of piston motion in a condition where the piston is sticking.
- the controller determines at time T1 that the actuator has not moved sufficiently and increases the actuation load such that the piston is actuated more suddenly to its displaced condition S1 .
- This invention uses the terms of the control system 8A, 8B and 8C as inputs to the condition monitoring function in order to provide greater understanding of the asset operation. Also the condition monitoring function plots/trends the changes in the controller internal signals over time. This allows the changes in controller internal signals to be compared to a standard or desired profile for those signals. This method may be simple (thresholds) or complex (models) depending on the requirement.
- a desired profile is accompanied by threshold values, typically for time and/or magnitude, such that a comparison of the recorded profile against the desired profile for said signals leads to a discrepancy which exceeds one or more of the predetermined thresholds.
- This may require, for example, a form, model or normalisation process so the operating context of the asset or component thereof can be eliminated.
- the proposed implementation of the invention would graph the terms of the control system signals and compare some or all of the sample points with a known good graph and assess its condition. Thereby, using the signals/parameters of the control loop itself, more condition information can be extracted (such as resistance to motion, discontinuities in motion, etc) to give a more accurate condition indicator without the addition of sensors.
- the individual signals 8A-8C allow improved fault isolation over simple controller effort monitoring. That is to say, different internal signal profiles create different dynamics and allow fault isolation.
- the derived information can be used to identify a number of different abnormal features in the controller behaviour with greater accuracy than by simply using the control output of the controller 6 to the asset 2.
- deviations in signal 8A alone may imply a different fault or abnormality in behaviour from, for example, a deviation in both signals 8A and 8B.
- deviations in those signals may be used to identify for example, step changes in behaviour as opposed to slower deviations over time, and account for corrective actions taken by the controller to maintain a desired operation of the asset.
- the above-described internal signals are output from the controller 6 to the change detection element 7 where monitoring and analysis of the received data is carried out.
- the change detection element can include multiple data inputs, including both the internal control parameters of the controller and also other external parameters as might exist in a conventional equipment health monitoring system.
- the change detection element 7 may receive measurement data from sensors 5 and/or data concerning the
- Such inputs to the element 7 may be achieved by direct or indirect connection to the sensors themselves or the controller input/outputs.
- the analysis of these signals performed by the change detection element may include mathematical manipulations and equipment health monitoring techniques such as trend monitoring, sequence detection, limits, statistical testing (e.g. student distribution "t” test), control charts techniques (e.g. CUSUM or Shewhart charts, etc.) and model comparison.
- the input to the change detection element may be processed using any existing techniques that may be applied to sensor data received by the change detection element.
- the output of the change detection element commands an action. Such actions might be: to alert the equipment operators to a determined minor fault; to schedule
- shut off means or mechanisms such as a shut off valve, relay or solenoid for example. Any, or any combination, of these actions may be performed either on-board or else remotely from the asset and may relate to the entire asset or any component or sub- assembly thereof.
- FIG. 4 there is shown an overview of a system 10 in which the present invention may be incorporated.
- a plurality of gas turbine engines 12 are depicted which are in service or On wing' for a fleet of aircraft. Whilst an aircraft fleet scenario is referred to below, it will be appreciated that the invention can be applied to other gas turbine engine scenarios, including a single aircraft or engine, or else a gas turbine engine used for other applications, such as power generation, marine
- each engine 12 Data relating to the operation of each engine 12 is collected over the engine operational life using conventional sensors and comprises a measure of the duration of operation of the engine and a variety of other operational measures such as fuel consumption, operation speeds or more detailed reports of performance as are common under conventional equipment health monitoring (EHM) practices.
- EHM equipment health monitoring
- Conventional sensors known to those skilled in the art are located on an engine or aircraft to generate readings of any or any combination of fuel consumption or flow, operating time, cycle time or frequency, operational speeds (such as rotor speeds), temperatures, pressures (such as air pressure), forces and the like, as well as operational context, such as for example Weight on Wheels (WoW) signals, engine operator inputs via manual controls, other engine demands, or the like.
- WoW Weight on Wheels
- the operational data for the engines 12, including the internal controller signals described above, is communicated to a remote or central control and/or monitoring facility, where records for all engines in the fleet are gathered. This is achieved by transmission of operational data, typically at the end of each aircraft flight, from the engine or associated aircraft to a control centre server 14.
- one or more wireless transmitters 16 associated with each engine transmit data signals to a receiver 18, which may comprise a base station, for example within a cellular network.
- the data is transmitted from the receiver 18 to the server 14 via a wide area network (WAN) such as the internet 20.
- WAN wide area network
- operational data may include different wireless data transmission standards or protocols or else a wired connection between an engine, or aircraft, and the internet 20.
- data may be transmitted in flight via satellite to ground.
- operational data may be recorded to a removable data storage device such as a memory stick or laptop for subsequent retrieval by and/or transmission to the server 14.
- the server 14 is associated with a network 22, typically via a secure local area or wide area network, over which the operational data can be disseminated for processing and or analysis using networked work stations 24.
- server 14 and network 22 is generally described herein as a monitoring or control centre and may comprise an asset monitoring service provider or else the asset operator organisation.
- the operational data may be communicated to both a service provider and also the asset operator. This is depicted by another server 14A and secure network 22A. Operational data may be transmitted to both servers 14 and 14A or else to the service provider only.
- the service provider may then process the data and make available a subset of data or else the results of the data processing to the asset operator, either by transmission thereof or else by hosting a web site which is accessible to the asset operator via the internet 20 or other network.
- the operational data including the internal signals from the controller, is processed so as to allow appropriate actions to be undertaken, such as the communication of information, instructions and/or control signals derived from the operational data and by the monitoring facility to the engines or operators thereof. It is also possible that such processing could be carried out onboard an aircraft or else by processing means mounted on an engine 12. Necessary actions could then be taken by the local/on-board monitoring device and/or
- the monitoring function is server- based in order to provide for quality control and continuity.
- the monitoring unit on, or associated with the asset would perform a first stage of data processing to determine the operational condition of the asset. If a normal asset operation is determined, then only summary data or a subset of the data need be transmitted to the monitoring facility. However if an unfavourable condition or else a fault is determined by the monitoring unit, then a larger volume of data pertaining to said condition or fault will be transmitted.
- the system would also allow for a mass offload of operation data from the monitoring unit in certain circumstances.
- the asset comprises both an electronic engine controller (EEC) 26 and an engine monitoring unit (EMU) 28 which are in communication, at least for
- a data bus 30 which is typically a conventional engine or aircraft data bus.
- a bespoke wired connection may also be established for this purpose.
- a suitable connection may be achieved using wireless communication technology, such as Wi-Fi (RTM), Bluetooth (RTM), or similar.
- data from the sensors may be received by a conventional wired arrangement, by way of a so-called harness, or else using suitable wireless transmission means so as to establish a suitable communication network on the engine.
- the EMU 28 gathers the necessary data, including the internal signal data as described above from the data bus 30 and records and conditions the data needed for EHM purposes for secure transmission to the monitoring centre in the manner described above, where the data is received and processed and the necessary resulting actions determined.
- the data may be stored both locally and remotely.
- the data will typically be encrypted for secure transmission.
- the data may also be compressed for
- the embodiments of the invention described below are applied to engines for a fleet of aircraft but may equally be applied to other high value assets which are owned by - or else at the disposal of - an asset operator and which require monitoring to achieve a proposed service life.
- the invention is particularly suited to high value assets and/or assets having a significant number of different failure modes.
- the term 'maintenance' used above refers to any kind of action which may be required to ensure correct functioning of an asset, including asset inspection, checking, testing, servicing, repair, overhaul, recall, adjustment, renovation, cleaning or the like.
- the present invention derives from a system or method for machine management, which may typically be carried out over a network comprising data transmitting and receiving actions, as well as the associated transmitting and receiving devices or hardware. Accordingly the transmitting and receiving ends of the system, as well as the method undertaken thereby, should each be considered to comprise embodiments or aspects of the invention in their own right, in addition to the system as a whole.
Abstract
An asset condition monitoring system having a plurality of sensors for taking readings of operational variables of the asset and a feedback controller. The feedback controller is arranged to receive data indicative of the operational variables measured by said sensors and to generate one or more internal signals. Those internal signals are used by the feedback controller to generate control instructions for controlling operation of the asset. The system also has a condition monitoring device arranged to receive one or more of the internal signals from the feedback controller and to determine an operational condition of the asset based there-upon. The monitoring unit may also receive the control instructions from the feedback controller and/or the operational data from the sensors.
Description
CONDITION MONITORING OF A SYSTEM CONTAINING A
FEEDBACK CONTROLLER
The present invention relates to asset condition or health monitoring and more particularly to the monitoring of machines in service.
Asset health monitoring, commonly referred to as equipment health monitoring (EHM), is based around the premise of sensing a plurality of operational variables for an asset during use. The gathered data can be used to determine an operational state of the asset. Additionally the data can be processed to identify the current condition or health status of the equipment
The output of the EHM system provides information to an operator which can be used to manage the operation of the equipment, for example by controlling the equipment in a manner which is sympathetic to the condition of the equipment or else by scheduling suitable repair or maintenance work.
The level of sophistication of an EHM system is often determined by the value or complexity of the asset. More particularly, sophisticated EHM systems are most often implemented where the cost of maintenance work to be carried out on the assets is relatively high. This therefore demands that maintenance schedules are optimised so that maintenance can be carried out effectively at appropriate intervals and with minimal disruption to the asset operation. Many asset control systems, including gas turbine engine controllers, use feedback control as a means to achieve desired transient and steady-state performance. The benefits of such methods of control derive at least in part from the ability to cater for operational disturbances, which may be caused by any of the operating context, environment or equipment condition, including equipment faults. For example, a feedback controller typically controls variables for a system to ensure the desired operation in spite of such disturbances.
However the control system's compensation for the equipment's condition, by design, masks the changes the EHM system is intended to detect, thereby, potentially making the EHM systems less effective or less sensitive, which may delay, or completely mask the EHM systems ability to detect a problem in a timely manner.
Methods for detecting small faults are numerous, yet conventional methods often require additional sensors or complex design and resource-hungry implementation. That is to say, an increase in the level of sophistication of an EHM system typically increases cost and complexity.
It is known to undertake trending of process variables (measured parameters) in order to improve condition monitoring without necessitating additional sensors or processing. Examples of such trending are described in WO/2007/133,543, and also in M. R.
Maurya, R. Rengaswamy, and V. Venkatasubramanian, "Fault diagnosis using dynamic trend analysis: A review and recent developments, " Eng. Appl. Artif. Intell., vol. 20, no. 2, pp. 133-146, 2007.
It is an aim of the present invention to provide additional or alternative methods for improving the certainty with which asset condition monitoring can be performed. It is an additional or alternative aim of the present invention to provide an improved asset condition monitoring system which does not add substantially to the cost or complexity of asset hardware.
The present invention provides a condition monitoring system for an asset having a feedback controller, wherein the condition monitoring system is arranged to receive one or more internal signals of the feedback controller in order to determine an operational condition of the asset.
According to a first aspect of the invention, there is provided a condition monitoring system for an asset comprising: a plurality of sensors for taking readings of operational variables of the asset; a feedback controller arranged to receive data indicative of the
operational variables measured by said sensors and to issue control instructions for controlling operation of the asset; and a condition monitoring device arranged to receive one or more internal signals from the feedback controller so as to allow determination of an operational condition of the asset based there-upon.
According to a preferred embodiment, the feedback controller receives the data from the sensors as one or more inputs and processes the data in order to generate one or more outputs. Preferably the control instructions comprise at least one output. In one embodiment, the feedback controller processes the received sensor data using one or more algorithms and thereby generates the one or more internal signals.
Preferably the control instructions are based upon, derived from or determined from the one or more internal signals. Typically the control instructions and internal signals are not the same.
The internal signals may comprise a second or further output of the feedback controller. The feedback controller may be arranged to output said internal signal to the condition monitoring device automatically. The feedback controller may determine a difference between a current operational state of the asset and a desired operational state. The internal signal may comprise a parameter which is proportional to said difference.
Additionally or alternatively, the internal signal may comprise a parameter which is based on a rate of change of said difference. Additionally or alternatively, the internal signal may comprise a parameter which is based on a duration of divergence of the current and desired operational states. The one or more internal signals comprise any or any combination of a proportional, differential and/or integral parameter value based upon said difference.
The internal signal preferably relates to a parameter that displays persistence.
Typically the internal signal is trendable such that changes over time can be assessed by the condition monitoring device.
In one embodiment, the condition monitoring device is arranged to further receive the data indicative of the operational variables measured by said sensors. That data may be received directly from the sensors, typically over a network, or else may be received via the feedback controller. The condition monitoring device may determine an operating condition of the asset based upon a combination of the sensor data and the internal signals of the feedback controller.
The present invention is advantageous in that condition monitoring device can provide a greater degree of certainty in its interpretation of the asset condition by monitoring a further system variable, namely the internal signal(s) of the controller. Despite the fact that such internal signals are derived from the sensor data, this new input allows the condition monitoring device to better account for any changes made to the asset operation, which may otherwise serve to mask underlying problems, or symptoms of those problems, from the condition monitoring unit.
Furthermore, the above advantages are achieved without the need to add to the complexity of the asset hardware. That is to say, the system can use existing architecture to better effect. It will be appreciated to the skilled person that the provision of sensing equipment and the complexity of the network that is required to be established to communicate the associated data to/from the different
components/devices of an asset can add significant cost and complexity to, not only the asset hardware itself, but also the assembly and maintenance thereof.
The condition monitoring device may be arranged to receive the control instructions from the controller. The condition monitoring device may determine an operating condition of the asset based upon the internal signals of the feedback controller and any, or any combination, of the sensor data and/or the control instructions.
The condition monitoring device may be included in the feedback controller, for example to improve cost and design simplicity. The condition monitoring device may comprise one or more processors which are separate to the control feedback
processor(s) but housed within a common unit. The condition monitoring device and the control feedback device may exist on common processing means, for example being separated by a soft partition. According to a second aspect of the invention there is provided a method of asset condition monitoring comprising: obtaining or receiving readings of asset operational variables from a plurality of sensors; obtaining or receiving an internal signal from an asset feedback controller; and, determining an operational state of the asset based on the combined readings and internal controller signal.
The method may be undertaken continually or substantially continually during an instance, or period, of asset operation. The sampling of data in time-spaced
increments, with a view to approximating continuous monitoring of the asset should be considered to be encompassed by reference to 'substantially continuous' data sampling or 'substantially continually' performing of the method of the invention.
The sampling interval of the data may be varied depending on the know operating state, and/or level of interest in the operating state. For instance, where precursors of a failure have been detected the sampling rate may be increased to provide greater sensitivity.
The condition monitoring methods are many, including, but not limited to, exceedance of thresholds to comparisons of "actual" condition monitoring signals with model based predictions.
The method may output information or instructions derived from the determination of asset operational state, such as asset operation instructions , asset condition information and/or an asset maintenance instruction. According to a third aspect of the invention there is provided a data carrier comprising computer readable instructions for controlling the operation of one or more processors to perform the method of the second aspect.
According to a further aspect of the invention, there is provided a condition monitoring system for an asset having a feedback controller, the condition monitoring system comprising: a plurality of sensors for taking readings of operational variables of the asset; a condition monitoring device arranged to receive data indicative of the operational variables measures by said sensors, wherein the condition monitoring device further receives one or more internal signals from the feedback controller so as to allow determination of an operational condition of the asset based upon both the sensor readings and also the internal signal from the feedback controller. Any preferable features defined herein in relation to the first aspect of the invention may be applied to the second, third or further aspects of the invention or the corresponding claims therefore, wherever practicable.
Any reference made herein to 'an asset' or 'equipment' may comprise a reference to a sub-assembly or component thereof.
Practicable embodiments of the invention are described below in further detail with reference to the accompanying drawings, of which: Figure 1 shows a schematic of a condition monitoring system according to the general principles of the present invention;
Figure 2 shows a flow diagram of exemplary control signals of the controller according to one embodiment of the invention;
Figure 3 is an exemplary plot of a measured variable against time for a machine;
Figure 4 shows an exemplary system of the present invention for gas turbine engines; and,
Figure 5 is a schematic showing further details of the flow of data in the system of Figure 4.
The present invention derives in general from the realisation by the inventor that the processing of data by the controller of an asset and the associated data signals used in the determination of control steps for the asset can be used to gain a greater insight into the health of the asset for condition monitoring purposes.
An asset as referred to in the description below typically refers to a machine or a number of machines, which are inter-reliant for correct operation thereof.
Computer control systems are used conventionally to operate machinery according to a control strategy. Simple control strategies may be used to control devices having a single, or relatively few, degrees of freedom such as valves, pistons, simple rotating drives and the like. However more complicated control strategies are put in place where a machine or system has a number of interdependent sub-assemblies or components, each of which having a number of control inputs and outputs.
Accordingly, controllers applying such control strategies typically receive operation data from, and have control over, a number of different sub-assemblies or components of the asset.
Turning now to Figure 1 , there is shown a control system 1 for a generic asset 2, which in this example is shown as a plant. During operation of the asset, there are one or more inputs 3 into the asset 2 and one or more outputs 4 from the asset. The inputs may be, for example, materials, energy and/or operation demands and the outputs may comprise any, or any combination, of products, energy, waste materials or the like. During operation of the asset 2, a number of sensors 5 sense different operational variables, or the same operational variables at different locations, and the sensor readings are fed to an asset controller 6 which runs a series, or nest, of control loop programs in its software in order to generate suitable control signals for operation of the asset 2 to produce the desired output 4. During this process, internal control signals are generated as will be described by way of example with reference to Figure 3.
Those internal signals are passed to an asset monitoring unit 7, which in this example may be referred to as a change detection element or unit, to be monitored.
The change detection element 7 compares data it receives pertaining to an operational state of the asset and compares it to a model which defines a normal or acceptable operational state of the asset. Whilst such functionality represents one implementation for a system according the invention there are other ways of detecting or assessing changes in operational condition, such as for example by comparison of received data with a fault model in addition to, or instead of, a normal model. Any such a model will typically be stored at the change detection element or else be accessible thereby and comprises data pertaining to acceptable or threshold value ranges for the sensor readings and internal signals of the feedback controller. The change detection element 7, in different embodiments of the invention, may be provided as a further processing means within the same hardware as the controller 6 or else may be located remotely from the controller and in data communication therewith. For example the change detection element may be co-located with the controller 6; located elsewhere on the same asset and connected therewith over a local network; or else remotely located and in communication with the controller over a wider network.
Turning now to Figure 2, there is shown the operation of a controller 6 with reference to the asset 2. The example given here is for a so-called proportional, differential, integral (PID) controller although other types of controller may be used. PID controllers apply a control feedback loop and are used to control a change in a machine from its current state to a currently desired, or reference, state.
In operation, the controller 6 receives a desired reference condition or state and compares it to a current measured state, which is based on the data received from sensors 5, indicative of measurements of operational variables for the asset. The controller then aims to rectify the difference between the current and desired states (i.e. the control error) by instructing one or more changes in the operation of the asset 2. This is otherwise described as applying control effort. The control effort, is controlled based on a combination of: a value proportional to the difference between the current state and the desired state (P), the rate of change
between the two states (differential, D) and the duration of the divergence between the current and reference states (integral, I). As shown in Figure 2, different functions are used to calculate the P, I and D values, which are numbered respectively as internal signals 8A, 8B and 8C. A summation of the resultant internal signals 8A-8C is then performed in order to determine the control effort required to change the operational state of the asset from its current state to its desired state.
Depending on the particular machine and experience of the operation thereof, the P, I and D signal elements may be weighted as part of the summation process in order to achieve suitable control of the machine. It will be appreciated that for some types of machine and/or control scenarios, only a simple P signal will be required, whereas in other situations a combination of two or more signals may be needed in order to achieve an optimal or stable control strategy. The control output from the controller 6 is then fed to the asset 2 and the resulting change in operation is measured and input to the start of the process as a feedback loop. Accordingly the control loop is repeated based on the changed state (or the new current state) of the asset. Thus the control feedback loop typically operates
continuously or intermittently, based upon a desired sampling rate or control frequency, during operation of the machine.
Whilst in a conventional system the controller outputs only control instructions, a controller performs a number of internal or intermediate steps or loops in order to arrive at those instructions. Accordingly the present invention seeks to access the internal or intermediate signals of the controller that contribute towards the final set of control instructions in order to glean useful EHM data there-from. Whilst the current example refers to those internal signals as P, I or D signal elements, it will be appreciated that a controller may use any of a number of different control strategies and may generate or handle a multitude of different internal parameter values. The term 'internal signals' is intended to encompass any such data that may otherwise not be available in a control signal output by the feedback controller. For example, a controller may operate a
number of different loops depending on the operational context of an asset (e.g.
depending on whether a gas turbine engine, for example, is idle, undergoing ignition, or different modes of operation). In one example of the present invention, simply knowing which control loop is in operation at a point in time, may provide the EHM system with additional information which can contribute to an operational condition assessment.
In Figure 3, an example is shown of a plot of displacement 's' against time 't' for an actuator, such as a hydraulic actuator. The dashed line represents a desired hydraulic actuation of a piston within a cylinder, whereas the solid line represents an actual plot of piston motion in a condition where the piston is sticking. For simplicity any overshoot or ringing has been omitted from the diagram. A severely sticking piston will take longer to complete the desired displacement under a corresponding hydraulic load. However in this example, the controller determines at time T1 that the actuator has not moved sufficiently and increases the actuation load such that the piston is actuated more suddenly to its displaced condition S1 .
The repetition of this rapid actuation of the piston under increased loading may adversely affect the operation of the machine over time. However a simple monitoring system which monitored the piston stroke only using an end stop sensor at displaced condition S1 would not observe the continuing abnormal piston operation since the piston maintains the desired piston stroke within the desired time frame (i.e. it achieves S1 at T2). Accordingly subtle faults in a number of machines and scenarios may not be observable by sensor outputs alone as the controller effort can compensate for the extra effort needed to maintain a desired operational output.
In another scenario, it is possible that the actual, recorded, movement of the piston could match the desired "shape" shown in the graph of Figure 3. However an isolated condition monitoring system would not be aware if the control system was achieving its desired motion in a normal operating condition or was having to compensate to achieve its movement. Where the controller is correcting for otherwise abnormal behaviour of
the asset, a plot or analysis of the Integral signal 8B of the internal controller signals by the EHM system would reveal the corresponding discrepancy even if the asset sensors are recording an otherwise normal machine operation. Critically, for an EHM system, early detection of the onset of failures increases the value of the function (more time to plan repairs or manage the operation). The earlier detection enabled by this invention increases its value.
This invention uses the terms of the control system 8A, 8B and 8C as inputs to the condition monitoring function in order to provide greater understanding of the asset operation. Also the condition monitoring function plots/trends the changes in the controller internal signals over time. This allows the changes in controller internal signals to be compared to a standard or desired profile for those signals. This method may be simple (thresholds) or complex (models) depending on the requirement.
Monitoring of the controller signals over time can then be used to determine any abnormal features in the internal signals which may be indicative of the controller taking measures to correct an unfavourable asset operational condition.. In this regard a desired profile is accompanied by threshold values, typically for time and/or magnitude, such that a comparison of the recorded profile against the desired profile for said signals leads to a discrepancy which exceeds one or more of the predetermined thresholds.
This may require, for example, a form, model or normalisation process so the operating context of the asset or component thereof can be eliminated.
The proposed implementation of the invention would graph the terms of the control system signals and compare some or all of the sample points with a known good graph and assess its condition. Thereby, using the signals/parameters of the control loop itself, more condition information can be extracted (such as resistance to motion, discontinuities in motion, etc) to give a more accurate condition indicator without the
addition of sensors. The individual signals 8A-8C allow improved fault isolation over simple controller effort monitoring. That is to say, different internal signal profiles create different dynamics and allow fault isolation. The derived information can be used to identify a number of different abnormal features in the controller behaviour with greater accuracy than by simply using the control output of the controller 6 to the asset 2. For example, deviations in signal 8A alone may imply a different fault or abnormality in behaviour from, for example, a deviation in both signals 8A and 8B. In different combinations, deviations in those signals may be used to identify for example, step changes in behaviour as opposed to slower deviations over time, and account for corrective actions taken by the controller to maintain a desired operation of the asset.
Returning now to Figure 1 , the above-described internal signals are output from the controller 6 to the change detection element 7 where monitoring and analysis of the received data is carried out. In various embodiments, the change detection element can include multiple data inputs, including both the internal control parameters of the controller and also other external parameters as might exist in a conventional equipment health monitoring system. For example, the change detection element 7 may receive measurement data from sensors 5 and/or data concerning the
environmental or operational context of the asset being monitored.
Such inputs to the element 7 may be achieved by direct or indirect connection to the sensors themselves or the controller input/outputs. The analysis of these signals performed by the change detection element may include mathematical manipulations and equipment health monitoring techniques such as trend monitoring, sequence detection, limits, statistical testing (e.g. student distribution "t" test), control charts techniques (e.g. CUSUM or Shewhart charts, etc.) and model comparison. In this regard, the input to the change detection element may be processed using any existing techniques that may be applied to sensor data received by the change detection element.
The output of the change detection element commands an action. Such actions might be: to alert the equipment operators to a determined minor fault; to schedule
maintenance or repair work for the asset; to apply hard operational limits via the control system such that the asset operation is maintained within a predetermined safe zone of operation so as to avoid potential damage or aggravation of the determined problem; or even to command that the equipment be shut down via the control system or via other shut off means or mechanisms, such as a shut off valve, relay or solenoid for example. Any, or any combination, of these actions may be performed either on-board or else remotely from the asset and may relate to the entire asset or any component or sub- assembly thereof.
Turning now to Figure 4, there is shown an overview of a system 10 in which the present invention may be incorporated. A plurality of gas turbine engines 12 are depicted which are in service or On wing' for a fleet of aircraft. Whilst an aircraft fleet scenario is referred to below, it will be appreciated that the invention can be applied to other gas turbine engine scenarios, including a single aircraft or engine, or else a gas turbine engine used for other applications, such as power generation, marine
propulsion, or other drive or pump applications. Data relating to the operation of each engine 12 is collected over the engine operational life using conventional sensors and comprises a measure of the duration of operation of the engine and a variety of other operational measures such as fuel consumption, operation speeds or more detailed reports of performance as are common under conventional equipment health monitoring (EHM) practices. Conventional sensors known to those skilled in the art are located on an engine or aircraft to generate readings of any or any combination of fuel consumption or flow, operating time, cycle time or frequency, operational speeds (such as rotor speeds), temperatures, pressures (such as air pressure), forces and the like, as well as operational context, such as for example Weight on Wheels (WoW) signals, engine operator inputs via manual controls, other engine demands, or the like.
The operational data for the engines 12, including the internal controller signals described above, is communicated to a remote or central control and/or monitoring facility, where records for all engines in the fleet are gathered. This is achieved by transmission of operational data, typically at the end of each aircraft flight, from the engine or associated aircraft to a control centre server 14. In the embodiment shown one or more wireless transmitters 16 associated with each engine transmit data signals to a receiver 18, which may comprise a base station, for example within a cellular network. The data is transmitted from the receiver 18 to the server 14 via a wide area network (WAN) such as the internet 20.
It will be appreciated that a variety of methods for transmission of operational data may be used which may include different wireless data transmission standards or protocols or else a wired connection between an engine, or aircraft, and the internet 20. For example, data may be transmitted in flight via satellite to ground. Alternatively operational data may be recorded to a removable data storage device such as a memory stick or laptop for subsequent retrieval by and/or transmission to the server 14. The server 14 is associated with a network 22, typically via a secure local area or wide area network, over which the operational data can be disseminated for processing and or analysis using networked work stations 24.
The combination of server 14 and network 22 is generally described herein as a monitoring or control centre and may comprise an asset monitoring service provider or else the asset operator organisation. Depending on the particular setup for asset monitoring and control, the operational data may be communicated to both a service provider and also the asset operator. This is depicted by another server 14A and secure network 22A. Operational data may be transmitted to both servers 14 and 14A or else to the service provider only. The service provider may then process the data and make available a subset of data or else the results of the data processing to the asset operator, either by transmission thereof or else by hosting a web site which is accessible to the asset operator via the internet 20 or other network.
In any of the above described embodiments, the operational data, including the internal signals from the controller, is processed so as to allow appropriate actions to be undertaken, such as the communication of information, instructions and/or control signals derived from the operational data and by the monitoring facility to the engines or operators thereof. It is also possible that such processing could be carried out onboard an aircraft or else by processing means mounted on an engine 12. Necessary actions could then be taken by the local/on-board monitoring device and/or
subsequently communicated to the relevant monitoring or control centre and/or engine operator as necessary. In the proposed implementation of the invention, the monitoring function is server- based in order to provide for quality control and continuity.
In a practicable implementation of the invention it is likely that a significant volume of the gathered asset operation data would not be transmitted to the monitoring facility. Instead the monitoring unit on, or associated with the asset, would perform a first stage of data processing to determine the operational condition of the asset. If a normal asset operation is determined, then only summary data or a subset of the data need be transmitted to the monitoring facility. However if an unfavourable condition or else a fault is determined by the monitoring unit, then a larger volume of data pertaining to said condition or fault will be transmitted. The system would also allow for a mass offload of operation data from the monitoring unit in certain circumstances.
Turning now to Figure 5, the onboard architecture and associated data flows are shown for an embodiment of a gas turbine engine 12 of Figure 4. In such an arrangement, it can be seen that the asset comprises both an electronic engine controller (EEC) 26 and an engine monitoring unit (EMU) 28 which are in communication, at least for
dissemination of data from the EEC to the EMU, but typically also for two-way communication of data, via a data bus 30, which is typically a conventional engine or aircraft data bus. A bespoke wired connection may also be established for this purpose. Additionally or alternatively a suitable connection may be achieved using wireless communication technology, such as Wi-Fi (RTM), Bluetooth (RTM), or similar.
Similarly, data from the sensors may be received by a conventional wired arrangement, by way of a so-called harness, or else using suitable wireless transmission means so as to establish a suitable communication network on the engine. In this embodiment, the EMU 28 gathers the necessary data, including the internal signal data as described above from the data bus 30 and records and conditions the data needed for EHM purposes for secure transmission to the monitoring centre in the manner described above, where the data is received and processed and the necessary resulting actions determined. In this manner the data may be stored both locally and remotely. The data will typically be encrypted for secure transmission. Depending on the volume of data to be transmitted, the data may also be compressed for
transmission.
The embodiments of the invention described below are applied to engines for a fleet of aircraft but may equally be applied to other high value assets which are owned by - or else at the disposal of - an asset operator and which require monitoring to achieve a proposed service life. The invention is particularly suited to high value assets and/or assets having a significant number of different failure modes. The term 'maintenance' used above refers to any kind of action which may be required to ensure correct functioning of an asset, including asset inspection, checking, testing, servicing, repair, overhaul, recall, adjustment, renovation, cleaning or the like.
It will be appreciated that the present invention derives from a system or method for machine management, which may typically be carried out over a network comprising data transmitting and receiving actions, as well as the associated transmitting and receiving devices or hardware. Accordingly the transmitting and receiving ends of the system, as well as the method undertaken thereby, should each be considered to comprise embodiments or aspects of the invention in their own right, in addition to the system as a whole.
Claims
An asset condition monitoring system comprising:
a plurality of sensors for taking readings of operational variables of the asset;
a feedback controller arranged to receive data indicative of the
operational variables measured by said sensors and to generate one or more internal signals, said internal signals being used by the feedback controller to generate control instructions for controlling operation of the asset; and,
a condition monitoring device arranged to receive one or more of said internal signals from the feedback controller and to determine an operational condition of the asset based there-upon. A condition monitoring system according to claim 1 , wherein the condition monitoring device is arranged to assess whether the determined operational condition is a normal or abnormal operating condition. A condition monitoring system according to claim 2, wherein in the event of abnormal operating condition determination, the condition monitoring device is arranged to output any or any combination of:
an asset condition or fault alert signal;
a signal scheduling maintenance or repair work for the asset;
a signal for applying operational limits to the asset; and/or
an asset shut down signal. A condition monitoring system according to any preceding claim, wherein the feedback controller operates one or more feedback loops and the internal signal comprises data pertaining to, or generated by, said feedback loop. A condition monitoring system according to any preceding claim, wherein the feedback controller determines a difference between a current operational state
of the asset and a desired asset state and the one or more internal signals comprise any or any combination of a proportional, differential and/or integral parameter value based upon said difference. A condition monitoring system according to any preceding claim, wherein the condition monitoring device is arranged to further receive the data indicative of the operational variables measured by said sensors and to determine an operating condition of the asset based upon a combination of the sensor data and the internal signals of the feedback controller. A condition monitoring system according to any preceding claim, wherein the condition monitoring device is arranged to further receive the control instructions output from the feedback controller determine an operating condition of the asset based upon a combination of the control instructions and the internal signals of the feedback controller. A condition monitoring system according to any preceding claim, wherein the condition monitoring device is located remotely of the asset, the system further comprising a transmitter device arranged to receive said internal signals from the feedback controller and to forward said internal signals to said condition monitoring device. A condition monitoring system according to any preceding claim, wherein the condition monitoring device is provided at a remote asset monitoring facility. A condition monitoring system according to any preceding claim, further comprising a data store having stored therein data pertaining to a predetermined normal operating state of said asset, wherein the data store is accessible by or integral with the condition monitoring device. An asset condition monitoring device for use in conjunction with an asset feedback controller, the device comprising:
a receiver arranged to receive a data signal from the feedback controller, said data signal comprising one or more internal parameter values used by the feedback controller to generate control instructions for controlling operation of the asset;
one or more processors arranged to process the received data signal and to determine an operational condition of the asset based upon said one or more internal parameter values of the feedback controller. An asset condition monitoring device according to claim 1 1 , said device being located on or adjacent the asset. An asset condition monitoring device according to claim 1 1 or claim 12, wherein the device is integral with or connected to the feedback controller. An asset condition monitoring device according to claim 1 1 , suitable for use in the system of any one of claims 1 to 10. A method of asset condition monitoring comprising:
receiving at a feedback controller data pertaining to readings of asset operational variables from a plurality of sensors;
generating one or more internal parameters within the feedback controller for use in providing control instructions for to control the operation of the asset; obtaining or receiving a data signal comprising one or more of said internal parameters from the feedback controller at a condition monitoring device; and,
determining an operational state of the asset based on the internal parameters of the feedback controller. The method of claim 15, wherein the generating one or more internal parameters within the feedback controller is undertaken substantially continually during a period of asset operation and wherein the condition monitoring device
determines the operation state of the asset over said period based on the plurality of generated internal parameters.
17 A data carrier comprising one or more modules of computer readable
instructions for controlling the operation of one or more processors to:
obtain or receive a data signal comprising one or more internal parameters of a feedback controller for a machine;
process said internal parameters to determine an operational state of the machine; and,
compare the determined operational state to predetermined operational state data to assess whether the determined operational state is a normal or abnormal state.
18 A data carrier according to claim 17, wherein the one or more processors are controlled by the computer readable instructions to compare the determined operational state to an asset operation model.
19 An asset condition monitoring system as claimed in any of claims 1 to 10 wherein the asset comprises a gas turbine engine.
20 An asset condition monitoring device as claimed in any of claims 1 1 to 14 wherein the asset comprises a gas turbine engine.
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Cited By (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9471452B2 (en) | 2014-12-01 | 2016-10-18 | Uptake Technologies, Inc. | Adaptive handling of operating data |
US9507365B2 (en) | 2014-06-24 | 2016-11-29 | Woodward, Inc. | Adaptive PID control system for industrial turbines |
US20160371584A1 (en) | 2015-06-05 | 2016-12-22 | Uptake Technologies, Inc. | Local Analytics at an Asset |
US10169135B1 (en) | 2018-03-02 | 2019-01-01 | Uptake Technologies, Inc. | Computer system and method of detecting manufacturing network anomalies |
US10176279B2 (en) | 2015-06-05 | 2019-01-08 | Uptake Technologies, Inc. | Dynamic execution of predictive models and workflows |
US10210037B2 (en) | 2016-08-25 | 2019-02-19 | Uptake Technologies, Inc. | Interface tool for asset fault analysis |
US10228925B2 (en) | 2016-12-19 | 2019-03-12 | Uptake Technologies, Inc. | Systems, devices, and methods for deploying one or more artifacts to a deployment environment |
US10255526B2 (en) | 2017-06-09 | 2019-04-09 | Uptake Technologies, Inc. | Computer system and method for classifying temporal patterns of change in images of an area |
US10291732B2 (en) | 2015-09-17 | 2019-05-14 | Uptake Technologies, Inc. | Computer systems and methods for sharing asset-related information between data platforms over a network |
US10333775B2 (en) | 2016-06-03 | 2019-06-25 | Uptake Technologies, Inc. | Facilitating the provisioning of a local analytics device |
US10379982B2 (en) | 2017-10-31 | 2019-08-13 | Uptake Technologies, Inc. | Computer system and method for performing a virtual load test |
US10474932B2 (en) | 2016-09-01 | 2019-11-12 | Uptake Technologies, Inc. | Detection of anomalies in multivariate data |
US10510006B2 (en) | 2016-03-09 | 2019-12-17 | Uptake Technologies, Inc. | Handling of predictive models based on asset location |
US10552246B1 (en) | 2017-10-24 | 2020-02-04 | Uptake Technologies, Inc. | Computer system and method for handling non-communicative assets |
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US10579750B2 (en) | 2015-06-05 | 2020-03-03 | Uptake Technologies, Inc. | Dynamic execution of predictive models |
US10579932B1 (en) | 2018-07-10 | 2020-03-03 | Uptake Technologies, Inc. | Computer system and method for creating and deploying an anomaly detection model based on streaming data |
US10579961B2 (en) | 2017-01-26 | 2020-03-03 | Uptake Technologies, Inc. | Method and system of identifying environment features for use in analyzing asset operation |
US10623294B2 (en) | 2015-12-07 | 2020-04-14 | Uptake Technologies, Inc. | Local analytics device |
US10635095B2 (en) | 2018-04-24 | 2020-04-28 | Uptake Technologies, Inc. | Computer system and method for creating a supervised failure model |
US10635519B1 (en) | 2017-11-30 | 2020-04-28 | Uptake Technologies, Inc. | Systems and methods for detecting and remedying software anomalies |
US10671039B2 (en) | 2017-05-03 | 2020-06-02 | Uptake Technologies, Inc. | Computer system and method for predicting an abnormal event at a wind turbine in a cluster |
US10796235B2 (en) | 2016-03-25 | 2020-10-06 | Uptake Technologies, Inc. | Computer systems and methods for providing a visualization of asset event and signal data |
US10815966B1 (en) | 2018-02-01 | 2020-10-27 | Uptake Technologies, Inc. | Computer system and method for determining an orientation of a wind turbine nacelle |
US10860599B2 (en) | 2018-06-11 | 2020-12-08 | Uptake Technologies, Inc. | Tool for creating and deploying configurable pipelines |
US10878385B2 (en) | 2015-06-19 | 2020-12-29 | Uptake Technologies, Inc. | Computer system and method for distributing execution of a predictive model |
US10975841B2 (en) | 2019-08-02 | 2021-04-13 | Uptake Technologies, Inc. | Computer system and method for detecting rotor imbalance at a wind turbine |
US11030067B2 (en) | 2019-01-29 | 2021-06-08 | Uptake Technologies, Inc. | Computer system and method for presenting asset insights at a graphical user interface |
US11119472B2 (en) | 2018-09-28 | 2021-09-14 | Uptake Technologies, Inc. | Computer system and method for evaluating an event prediction model |
US11181894B2 (en) | 2018-10-15 | 2021-11-23 | Uptake Technologies, Inc. | Computer system and method of defining a set of anomaly thresholds for an anomaly detection model |
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US11232371B2 (en) | 2017-10-19 | 2022-01-25 | Uptake Technologies, Inc. | Computer system and method for detecting anomalies in multivariate data |
US11295217B2 (en) | 2016-01-14 | 2022-04-05 | Uptake Technologies, Inc. | Localized temporal model forecasting |
US11480934B2 (en) | 2019-01-24 | 2022-10-25 | Uptake Technologies, Inc. | Computer system and method for creating an event prediction model |
US11797550B2 (en) | 2019-01-30 | 2023-10-24 | Uptake Technologies, Inc. | Data science platform |
US11892830B2 (en) | 2020-12-16 | 2024-02-06 | Uptake Technologies, Inc. | Risk assessment at power substations |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2516080A (en) * | 2013-07-11 | 2015-01-14 | Rolls Royce Plc | Health monitoring |
CN103869703A (en) * | 2014-03-28 | 2014-06-18 | 东华大学 | Wireless monitoring system based on PID controller of internal secretion single-neuron |
US20230230424A1 (en) * | 2022-01-20 | 2023-07-20 | Pratt & Whitney Canada Corp. | Method and system for data transmission from an aircraft engine |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2007133543A2 (en) | 2006-05-07 | 2007-11-22 | Applied Materials, Inc. | Ranged fault signatures for fault diagnosis |
EP2169496A1 (en) * | 2008-09-30 | 2010-03-31 | Rockwell Automation Technologies, Inc. | Modular condition monitoring integration for control systems |
US20110135475A1 (en) * | 2010-05-26 | 2011-06-09 | Udo Ahmann | Systems and methods for monitoring a condition of a rotor blade for a wind turbine |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7301296B1 (en) * | 2001-07-23 | 2007-11-27 | Rockwell Automation Technologies, Inc. | Integrated control and diagnostics system |
US8060340B2 (en) * | 2002-04-18 | 2011-11-15 | Cleveland State University | Controllers, observers, and applications thereof |
WO2005072264A2 (en) * | 2004-01-23 | 2005-08-11 | Gsi Lumonics Corporation | System and method for adjusting a pid controller in a limited rotation motor system |
-
2011
- 2011-09-07 GB GB201115410A patent/GB2494416A/en not_active Withdrawn
-
2012
- 2012-08-17 WO PCT/EP2012/066100 patent/WO2013034420A1/en active Application Filing
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2007133543A2 (en) | 2006-05-07 | 2007-11-22 | Applied Materials, Inc. | Ranged fault signatures for fault diagnosis |
EP2169496A1 (en) * | 2008-09-30 | 2010-03-31 | Rockwell Automation Technologies, Inc. | Modular condition monitoring integration for control systems |
US20110135475A1 (en) * | 2010-05-26 | 2011-06-09 | Udo Ahmann | Systems and methods for monitoring a condition of a rotor blade for a wind turbine |
Non-Patent Citations (2)
Title |
---|
M. R. MAURYA; R. RENGASWAMY; V. VENKATASUBRAMANIAN: "Fault diagnosis using dynamic trend analysis: A review and recent developments", ENG. APPL. ARTIT. INTELL., vol. 20, no. 2, 2007, pages 133 - 146, XP005786109, DOI: doi:10.1016/j.engappai.2006.06.020 |
YASAR ET AL: "Hierarchical control of aircraft propulsion systems: Discrete event supervisor approach", CONTROL ENGINEERING PRACTICE, PERGAMON PRESS, OXFORD, GB, vol. 15, no. 2, 1 February 2007 (2007-02-01), pages 149 - 162, XP005718289, ISSN: 0967-0661, DOI: 10.1016/J.CONENGPRAC.2006.05.011 * |
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