CA2604118C - A system and method for real-time prognostics analysis and residual life assessment of machine components - Google Patents
A system and method for real-time prognostics analysis and residual life assessment of machine components Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/14—Testing gas-turbine engines or jet-propulsion engines
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/80—Diagnostics
Abstract
A system for performing continuous (real-time) prognostics analysis and a method for the display of the changes in the fracture critical locations, life to flaw or crack nucleation and distortion and life consumption of multiple turbine engine components prior to the development of internal material damage and faults in the components. Each engine control system is connected to an interface and to a data analysis and a prognostics processor unit and routinely monitored engine parameters such as engine speed, turbine inlet or exit temperatures, ambient temperature and pressure and cooling airflow are measured in real-time. The system performs modified rain-flow and materials engineering rule based mission profile analysis for assessing variability in damage accumulation modes and mechanisms which is followed by combustor modeling to predict the combustion liner temperatures and combustion nozzle plane temperature distributions as a function of engine usage. This is further followed by off-design engine modeling to determine the pitch-line temperatures in hot gas path components and thermodynamic modeling to compute the component temperature profiles of gas path components for different stages of the turbine; followed by finite element (FE) based non-linear stress-strain analysis using a real-time FE solver and materials microstructure and internal state variable based damage accumulation and fracture analysis. The system further performs real- time remaining life analysis in the presence of flaws or distortion and predicts a safe inspection interval of multiple turbine engine components.
Description
Description Method and a system for real-time prognosis analysis and usage based residual life assessment of turbine engine components and display TECHNICAL FIELD
[0001] The present invention relates real-time monitoring of turbine engines with physics based prognostics analysis of multiple engine components for life consumption and residual life prediction. More specifically the invention relates to a method and a system for real time monitoring and physics based loads and damage analysis of multiple engine components as a function of actual engine usage including the display of the variability of fracture critical locations, life consumption and residual life using data collected real-time by traditional monitoring methods.
References Cited U.S. Patent Documents 4215412 July 1980 Bernier et al.
5080496 January 1992 Keim et al.
5689066 November 1997 Stevenson 6343251 January 2002 Herron et al.
6449565 September 2002 Budrow et al.
6539783 April 2003 Adibhatla 6756908 June 2004 Gass et al.
6845306 January 2005 Henry et al.
6871160 March 2005 Jaw 7197430 March 2007 Jacques et al.
Prior Art US Patent 4215412 by Bernier et al discloses the real time performance monitoring system for gas turbines but the performance data collected is not further processed to allow computation of component lives.
US Patent 5080496 by Keim et al discloses the sensor apparatus for monitoring gas path temperatures and engine rotational speeds and the sensor data collected is not further processed to allow computation of component lives.
US Patent 5689066 by Stevenson discloses the method and apparatus for analyzing gas turbine pneumatic fuel system using air pressure data to detect the development of damage or faults in the fuel control system. The system does not deal with the main body of the turbine.
US Patent 6343251 by Herron et al discloses the method and system for predicting life consumption of a gas turbine simply by keeping a record of engine operation and using a simple calculator to predict residual life by subtracting usage life from a predetermined life for the engine. The system does not actually compute component level temperatures, stresses etc. or possess any life prediction algorithms to predict real time residual life.
US Patent 6449565 by Budrow et al discloses a sensor based but real time stress-strain data collection system to determine stress pairs to compute usage based fatigue life. The proposed system is location specific on a structural membrane and its application in a gas turbine operating environment would be limited if not impossible.
US Patent 6539783 by Adibhatla discloses a turbine performance monitoring and estimation system for fault detection rather than life prediction. Most performance-based systems are only able to pick up compressor fowling in real life and the diagnostic capability of such systems is extremely limited.
US Patent 6756908 by Gass et al discloses an electronic sensor based crack detection system in a specific fracture critical location. The system is a location specific diagnostic system rather than a prognostics system proposed in our patent.
US Patent 6845306 by Henry et al discloses a performance monitoring and trending system and comparing the trends with historical data to detect component level faults. Again, most performance-based diagnostic systems are only able to pick up compressor fowling in real life and the diagnostic capability of such systems is extremely limited.
US Patent 6871160 by Jaw discloses the architecture of a machine health management system that uses sensor data, predetermined life, predicted failure modes information and planning and scheduling information to make decisions about condition based life cycle management of the machine. Predetermined life is not the same thing as the usage based life prediction and a user or an original equipment manufacturer almost have to loose half of the turbine fleet to realistically populate the failure modes data bases because physics based prognosis is not used to determine the failure modes.
US Patent 7197430 by Jacques et al discloses a sensor information based engine parts life usage system (EPLTS) to quantify cyclic and/or steady state usage but the system only uses predetermined component lives to make decisions about the remaining life of the components and the process does not involve physics based prognosis to compute life usage or residual life as a function of engine operation.
[0001] The present invention relates real-time monitoring of turbine engines with physics based prognostics analysis of multiple engine components for life consumption and residual life prediction. More specifically the invention relates to a method and a system for real time monitoring and physics based loads and damage analysis of multiple engine components as a function of actual engine usage including the display of the variability of fracture critical locations, life consumption and residual life using data collected real-time by traditional monitoring methods.
References Cited U.S. Patent Documents 4215412 July 1980 Bernier et al.
5080496 January 1992 Keim et al.
5689066 November 1997 Stevenson 6343251 January 2002 Herron et al.
6449565 September 2002 Budrow et al.
6539783 April 2003 Adibhatla 6756908 June 2004 Gass et al.
6845306 January 2005 Henry et al.
6871160 March 2005 Jaw 7197430 March 2007 Jacques et al.
Prior Art US Patent 4215412 by Bernier et al discloses the real time performance monitoring system for gas turbines but the performance data collected is not further processed to allow computation of component lives.
US Patent 5080496 by Keim et al discloses the sensor apparatus for monitoring gas path temperatures and engine rotational speeds and the sensor data collected is not further processed to allow computation of component lives.
US Patent 5689066 by Stevenson discloses the method and apparatus for analyzing gas turbine pneumatic fuel system using air pressure data to detect the development of damage or faults in the fuel control system. The system does not deal with the main body of the turbine.
US Patent 6343251 by Herron et al discloses the method and system for predicting life consumption of a gas turbine simply by keeping a record of engine operation and using a simple calculator to predict residual life by subtracting usage life from a predetermined life for the engine. The system does not actually compute component level temperatures, stresses etc. or possess any life prediction algorithms to predict real time residual life.
US Patent 6449565 by Budrow et al discloses a sensor based but real time stress-strain data collection system to determine stress pairs to compute usage based fatigue life. The proposed system is location specific on a structural membrane and its application in a gas turbine operating environment would be limited if not impossible.
US Patent 6539783 by Adibhatla discloses a turbine performance monitoring and estimation system for fault detection rather than life prediction. Most performance-based systems are only able to pick up compressor fowling in real life and the diagnostic capability of such systems is extremely limited.
US Patent 6756908 by Gass et al discloses an electronic sensor based crack detection system in a specific fracture critical location. The system is a location specific diagnostic system rather than a prognostics system proposed in our patent.
US Patent 6845306 by Henry et al discloses a performance monitoring and trending system and comparing the trends with historical data to detect component level faults. Again, most performance-based diagnostic systems are only able to pick up compressor fowling in real life and the diagnostic capability of such systems is extremely limited.
US Patent 6871160 by Jaw discloses the architecture of a machine health management system that uses sensor data, predetermined life, predicted failure modes information and planning and scheduling information to make decisions about condition based life cycle management of the machine. Predetermined life is not the same thing as the usage based life prediction and a user or an original equipment manufacturer almost have to loose half of the turbine fleet to realistically populate the failure modes data bases because physics based prognosis is not used to determine the failure modes.
US Patent 7197430 by Jacques et al discloses a sensor information based engine parts life usage system (EPLTS) to quantify cyclic and/or steady state usage but the system only uses predetermined component lives to make decisions about the remaining life of the components and the process does not involve physics based prognosis to compute life usage or residual life as a function of engine operation.
BACKGROUND OF THE INVENTION
[0002] Surface damage occurs to turbine parts as a result of erosion, corrosion, fretting, wear or impact by foreign objects and particulates. This loss of surface integrity and component geometry leads to losses in aerodynamic and thermal efficiency, and reduced power output for a given fuel burn. Parts suffer internal structural damage, which leads to metallurgical deterioration and ultimately component failure, engine shutdown and unscheduled maintenance. The cyclic nature of power demands and the high frequency pressure fluctuations due to turbulence in gas flows leads to internal structural damage in the form of fatigue in rotating parts such as turbine discs, shafts, spacers and blades. The components in the hot gas stream, such as turbine discs and blades, guide vanes, seals, and combustor casings and linings, suffer from cyclic fluctuations in temperature as well as inertia loads, both of which cause internal structural damage due to creep, thermal fatigue, thermal-mechanical fatigue (TMF) and/or high temperature low cycle fatigue (HTLCF), and various combinations of these mechanisms. The net result of the combined action of all of these damage modes is that many of these high cost components have finite lives. The challenge to manufacturers and the operators of engines is to determine when to inspect and overhaul the engine, and when to repair or replace the used parts, all of which involve downtime of equipment and high cost for manpower and replacement parts. Failure to deal adequately with any of these challenges may lead to unexpected failures, unscheduled shutdown and a cascade of damage to otherwise sound components. The state-of-the-art is such that worst-case assumptions for engine operating parameters and its operating environment in conjunction with empirical structural and damage analysis techniques and practical operating experience are used to anticipate the life-limiting modes of damage accumulation and to predict deterministic safe operating life of the different turbine engine components and to fix a predetermined major time between overhaul (TBO) intervals for the engine. Major overhaul is by far the most expensive maintenance action item during the life cycle management of turbine engines.
[0002] Surface damage occurs to turbine parts as a result of erosion, corrosion, fretting, wear or impact by foreign objects and particulates. This loss of surface integrity and component geometry leads to losses in aerodynamic and thermal efficiency, and reduced power output for a given fuel burn. Parts suffer internal structural damage, which leads to metallurgical deterioration and ultimately component failure, engine shutdown and unscheduled maintenance. The cyclic nature of power demands and the high frequency pressure fluctuations due to turbulence in gas flows leads to internal structural damage in the form of fatigue in rotating parts such as turbine discs, shafts, spacers and blades. The components in the hot gas stream, such as turbine discs and blades, guide vanes, seals, and combustor casings and linings, suffer from cyclic fluctuations in temperature as well as inertia loads, both of which cause internal structural damage due to creep, thermal fatigue, thermal-mechanical fatigue (TMF) and/or high temperature low cycle fatigue (HTLCF), and various combinations of these mechanisms. The net result of the combined action of all of these damage modes is that many of these high cost components have finite lives. The challenge to manufacturers and the operators of engines is to determine when to inspect and overhaul the engine, and when to repair or replace the used parts, all of which involve downtime of equipment and high cost for manpower and replacement parts. Failure to deal adequately with any of these challenges may lead to unexpected failures, unscheduled shutdown and a cascade of damage to otherwise sound components. The state-of-the-art is such that worst-case assumptions for engine operating parameters and its operating environment in conjunction with empirical structural and damage analysis techniques and practical operating experience are used to anticipate the life-limiting modes of damage accumulation and to predict deterministic safe operating life of the different turbine engine components and to fix a predetermined major time between overhaul (TBO) intervals for the engine. Major overhaul is by far the most expensive maintenance action item during the life cycle management of turbine engines.
[0003] Component level internal structural damage and distortion due to creep, LCF
or TMF, is difficult to detect, and only empirical models are available to guess damage accumulation rates and the critical levels of damage beyond which remedial action would be essential. Current deterministic practices use operating times or numbers of operating cycles required to initiate detectable flaws in a large population of parts under worst case usage, and the statistical distribution of this data is used to determine the lower bound threshold for component replacement, typically -3 standard deviations from mean. This is adopted as the predetermined safe-life limit for all parts, regardless of the fact that the vast majority will contain no detectable damage at this point, and hence have the capacity for further use.
Furthermore, several analyses described in the literature have shown that 999 parts in a typical population of 1000 would, on average, have 10 to 20 lifetimes remaining at this point. This life can be harnessed using inspection based life cycle management of parts but the inspection intervals under creep, fatigue and combined loading conditions can only be developed using physics based crack propagation modeling techniques.
or TMF, is difficult to detect, and only empirical models are available to guess damage accumulation rates and the critical levels of damage beyond which remedial action would be essential. Current deterministic practices use operating times or numbers of operating cycles required to initiate detectable flaws in a large population of parts under worst case usage, and the statistical distribution of this data is used to determine the lower bound threshold for component replacement, typically -3 standard deviations from mean. This is adopted as the predetermined safe-life limit for all parts, regardless of the fact that the vast majority will contain no detectable damage at this point, and hence have the capacity for further use.
Furthermore, several analyses described in the literature have shown that 999 parts in a typical population of 1000 would, on average, have 10 to 20 lifetimes remaining at this point. This life can be harnessed using inspection based life cycle management of parts but the inspection intervals under creep, fatigue and combined loading conditions can only be developed using physics based crack propagation modeling techniques.
[0004] Engine Parts Life Tracking Systems (EPLTS) have been developed to monitor life consumption and residual life of individual sets of components to schedule a TBO.
In these systems, the engine usage is tracked and the speed and temperature data are stored and analyzed to isolate cyclic usage from steady state usage and to isolate mechanical cycling from thermal cycling. However, the life consumption and residual life of different components in EPLTS based systems are still computed using pre-determined safe life limits as opposed to actual usage based predicted life.
In these systems, the engine usage is tracked and the speed and temperature data are stored and analyzed to isolate cyclic usage from steady state usage and to isolate mechanical cycling from thermal cycling. However, the life consumption and residual life of different components in EPLTS based systems are still computed using pre-determined safe life limits as opposed to actual usage based predicted life.
[0005] Condition based maintenance using diagnostics techniques for preventive maintenance have also been studied and these systems use relatively crude methods to monitor trends and major engine operating parameters such as temperatures and pressure ratios across different stages and fuel burn, from which gross changes in structural integrity may be inferred. Diagnostics based techniques, however, are only capable of picking gross faults and can be useful in preventing catastrophic failures but cannot be effectively used for residual life assessment purposes.
[0006] At present, there is no real-time prognostics system that has been developed for the predictive maintenance of multiple turbine engine components using physics based loads and damage analysis techniques. Over the last three decades, tremendous advances have been made in improving the engine performance monitoring and data collection and trending capability. These systems typically use sensors and numerous advances have been made in monitoring systems to provide alarms and improve displays However, apart from predicting compressor fowling, the inability of performance monitoring systems to assist with predictive maintenance and TBO prediction remains unchanged. Extensive basic scientific research has indicated that component level failure is caused by usage-based loads that are responsible for the development of damage at the microstructural level.
Therefore, continuous quantitative assessment of usage based thermal-mechanical loads and microstructural damage as a function of these service loads is vital for the development of any prognosis systems.
Therefore, continuous quantitative assessment of usage based thermal-mechanical loads and microstructural damage as a function of these service loads is vital for the development of any prognosis systems.
[0007] Following an exponential growth in the understanding and use of computational fluid dynamics techniques, the effect of thermal and aerodynamic loads on component level structural response has been extensively studied.
While identification of thermal boundary conditions is important for gauging the component level structural response, the effect of underlying deformation and fracture mechanisms on life consumption and residual life has not received equal attention.
Traditional research has focused on the computation of worst-case usage loads and the use of empirical damage modeling techniques to predict component level response to these worst-case thermal-mechanical loads. The use of empirical life prediction techniques also requires the generation of large but very expensive material databases along with a lot of field experience to accurately predict the future component behaviour.
While identification of thermal boundary conditions is important for gauging the component level structural response, the effect of underlying deformation and fracture mechanisms on life consumption and residual life has not received equal attention.
Traditional research has focused on the computation of worst-case usage loads and the use of empirical damage modeling techniques to predict component level response to these worst-case thermal-mechanical loads. The use of empirical life prediction techniques also requires the generation of large but very expensive material databases along with a lot of field experience to accurately predict the future component behaviour.
[0008] Evaluation of variability of component life as a result of variability in usage and microstructural features or stochastic material behaviour has only recently come under investigation in turbine engineering and materials science respectively, and is generally not used in routine turbine engineering practice for the life cycle management of engines. Variability describes the degree to which usage loads change over time and also changes in microstructural features from one component to another and how these microstructural parameters also change over time during service. The initial distributions of microstructure often exist in a set of components and dynamics of some microstructural variables change over time during service and these distributions and their changes govern the material response to usage based thermal-mechanical loads during future service. A parameter such as the grain size may have an initial distribution in a set of turbine blades and vanes but may remain relatively constant during service, demonstrating a low degree of dynamic variability.
Parameters such as intragranular precipitate size, grain boundary precipitate size and dislocation density may possess an initial distribution and their distributions may wildly change or shift with high variability during service. The initial as well as the dynamic variability of the microstructural features along with the variability in thermal-mechanical loads with time must all be considered for accurate life prediction.
Parameters such as intragranular precipitate size, grain boundary precipitate size and dislocation density may possess an initial distribution and their distributions may wildly change or shift with high variability during service. The initial as well as the dynamic variability of the microstructural features along with the variability in thermal-mechanical loads with time must all be considered for accurate life prediction.
[0009] The evaluation of inherent grain size variability has proven to contain valuable information regarding the creep behaviour of conventionally cast as well as forged components operating in high temperature and stress operating environments. It can provide accurate and reliable prognostic stratification of the risk of creep fracture in a population of components during service.
[0010] In addition, evaluation of grain boundary carbide variability due to primary carbide degeneration during service in cast turbine blades has revealed increased tendency for creep ductility reduction and material embrittlement.
[0011] Thus, initial and dynamic variability of microstructures in individual sets of components along with changes in in-service usage and operational conditions leading to variability in thermal-mechanical loads control their life consumption and residual life. The significance of the evaluation of the effect of individual variables that influence life indicates that the continuous evaluation of multiple components will provide useful and accurate information on the TBO status of the engine.
To date, there has been no attempt made to provide the engine users with the variability analysis of life consumption or residual life of multiple engine components on the basis of actual usage and usage based thermal-mechanical loads and damage analysis, nor provide the capability for continuous real-time variability analysis and display.
SUMMARY OF THE INVENTION
To date, there has been no attempt made to provide the engine users with the variability analysis of life consumption or residual life of multiple engine components on the basis of actual usage and usage based thermal-mechanical loads and damage analysis, nor provide the capability for continuous real-time variability analysis and display.
SUMMARY OF THE INVENTION
[0012] It is therefore an object of the invention to provide a method and a prognosis system for continuously evaluating the usage and operating environment based thermal-mechanical loads and damage accumulation in multiple engine components to accurately predict their life consumption and residual life as a function of usage in order to facilitate proactive repair and overhaul decisions, predict individualized and cost effective TBO for the engine, with the ultimate goal of reducing the cost of ownership of the engine.
[0013] Another object of the invention is to provide a tool to aid in the evaluation of the overall engine health by analyzing the changes in components in real-time, in order to provide a guide for remedial action.
[0014] The invention therefore provides a method of monitoring variability of life consumption and residual life of multiple engine components using data acquired from the engine monitoring interfaces, comprising steps of collecting data points acquired by each of the monitoring interfaces and analyzing these parameters to discern the type of loads generated during actual engine operation;
continuously computing a measure of the variability of thermal-mechanical loads including the effects of changing operating environment that in turn govern the degree to which the damage accumulation and life of different components fluctuates over time, and continuously displaying the variability for each of these TBO governing parameters.
continuously computing a measure of the variability of thermal-mechanical loads including the effects of changing operating environment that in turn govern the degree to which the damage accumulation and life of different components fluctuates over time, and continuously displaying the variability for each of these TBO governing parameters.
[0015] The invention further provides an real-time prognosis system for monitoring variability of engine usage parameters, comprising a system coupled to an engine monitor for receiving data points associated with the changes in engine operating parameters, the prognosis system being adapted for an engine to trigger analysis and continuously compute, for each of the components being monitored, a variability representative of an estimate of the degree to which the thermal-mechanical loads and damage accumulation fluctuates over time; and means for continuously displaying the variability in life consumption and residual life for each of the engine components being monitored.
[0016] The paradigm that underlies the invention is one of complex systems, where the focus is on the emergent multiple component level response to composite of service variables, load changes and damage accumulation processes or the response of the major components of the engine. The focus of the invention is to facilitate individualized engine care, under an assumption that different engines of the same type require completely different types of interventions for their life cycle management.
[0017] By providing simultaneous analysis and display of the variability of damage accumulation and life consumption of individual engine components using accurately measured engine parameters, the effect of different usage scenarios on component level response can be studied off-line. Variability of multiple usage parameters can be used to detect potential engine structural problems and the invention also permits studying alterations to the usage, component level design or material change in order to arrive at a solution to a potential structural problem.
[0018] Engine operational parameters capable of variability assessment include any parameter that can be accurately measured. The parameters are ideally measured at regularly recurring intervals and these include engine operating parameters (speed, exhaust gas temperature or turbine inlet temperature, power, inlet and exhaust pressure, bleed air fraction and others) and operating environment parameters (ambient temperature, ambient pressure, altitude). Patterns of variability include the analysis of how several parameters change over time in concert.
[0019] This continuous and simultaneous analysis of the variability of multiple engine parameters provides a means for removing the artefacts and real-time identification and differentiation between types of loads and the engine response to these loads.
[0020] The present invention provides for continuous and simultaneous variability analysis and display of multiple engine components, in order to analyze usage based:
[0021] combustor temperature profile and the pattern of variability in combustor nozzle plane temperature profile on a real-time basis, [0022] component temperature profiles and stress, strain and temperature values operative in different component locations, [0023] damage analysis for determining physics based life consumption and residual life under creep, low cycle fatigue, thermal fatigue, thermal-mechanical fatigue, cyclic oxidation, stress corrosion, corrosion fatigue, fretting wear, fretting fatigue, high cycle fatigue, oxidation, hot corrosion and combined loading conditions.
[0024] fracture critical locations, component distortion, component surface condition, crack nucleation life, crack propagation based safe inspection intervals that serve as a guide to select the most cost effective TBO.
PREFERRED FEATURES OF THE INVENTION
PREFERRED FEATURES OF THE INVENTION
[0025] The following illustrates various aspects of the present real-time system invention:
[0026] A method and a system for monitoring variability of usage based life consumption and residual life of multiple engine components real-time by monitoring engine operating parameters, comprising steps of: collecting engine operating parameters and analyzing these parameters and using real-time combustor modeling, off-design engine analysis, thermodynamic modeling and thermal-mechanical structural analysis to discern the type of loads generated during actual engine operation; continuously computing a measure of the variability of thermal-mechanical loads operative in different components that in turn govern the degree to which the damage accumulation variability evolves and life of different components fluctuates over time, and continuously displaying the variability for each of these TBO governing life prediction parameters.
[0027] The method for continuous and simultaneous analysis of the variability of multiple engine parameters provides a means for removing the artefacts and real-time identification and differentiation between types of loads and the engine response to these loads. The step of removing artefacts comprises the steps of using a combination of analysis techniques for engine speed variation and homologous temperature variability analysis to identify undesirable data points.
[0028] The method further comprises a step of selecting a method of computing the variability of combustor temperature profile due to variability of service operating conditions and the plurality of evolving combustor defects and damage parameters.
[0029] The method further comprises a step of selecting a method of computing the variability of thermal-mechanical load profile of components due to variability of service operating conditions and selecting which proflie contributes to what type of damage such as creep, LCF, TMF, cyclic oxidation, creep crack growth and fatigue crack growth or combined damage mechanisms.
[0030] The method further comprises a step of selecting a method of computing the variability of damage accumulation in components due to variability of service operating conditions and the plurality of evolving component microstructure and damage and selecting which microstructural features contribute to what type of damage such as creep, LCF, TMF, cyclic oxidation, creep crack growth and fatigue crack growth and the combined damage mechanisms.
[0031] The method, wherein the step of displaying comprises displaying an real-time correlation between the variability of engine parameters and the plurality of component condition parameters such as the five major fracture critical locations, residual life, distribution of the residual life of a set of components.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0032]Figure 1 presents the flow diagram of the real-time prognosis system invention.
[0033] Figure 2 is a block diagram of an embodiment of the mission profile analyzer;
[0034] Figure 3 is a typical output of the mission profile analyzer displaying cyclic loads and temperature variability;
[0035] Figure 4 is a block diagram of an embodiment of the combustor model having central variability analysis capability along with its own GUI and display;
[0036] Figure 5 is a typical output of the combustor model displaying combustor nozzle temperature variability;
[0037] Figure 6 is a flowchart illustrating the main steps of an embodiment of the off-design engine analysis and potential flow based thermodynamics analysis methods [0038] Figure 7 is a typical output of the off-design engine analysis and thermodynamic analysis models displaying engine temperature variability;
[0039] Figure 8 is a block diagram illustrating exemplary inputs and outputs of the real-time, non-linear finite element, solver and displays for individual variables;
[0040] Figure 9 illustrates exemplary variability of component temperatures, stresses and strains;
[0041] Figure 10 is a block diagram illustrating exemplary inputs and outputs of the real-time, physics based damage models and probabilistic displays for individual variables;
[0042] Figure 11 illustrates exemplary plots correlating data points for the variability of life in the FE model; and [0043] Figure 12 illustrates exemplary plots correlating data points for the probabilistic variability in life for a single fracture critical location in the FE model;
and [0044] Figure 13 illustrates exemplary plots correlating data points for the variability of life in probabilistic distribution of a primary, secondary and tertiary fracture critical locations.
and [0044] Figure 13 illustrates exemplary plots correlating data points for the variability of life in probabilistic distribution of a primary, secondary and tertiary fracture critical locations.
[0045] Figure 14 illustrates exemplary review displays of variability of life in the form of histograms.
[0046] Figure 15 Deterministic fracture mechanics (DFM) flow diagram and output for crack propagation interval and safe inspection interval prediction.
I
I
[0047] Figure 16 Probabilistic fracture mechanics (PFM) flow diagram and PFM
output for crack propagation interval prediction and risk assessment.
output for crack propagation interval prediction and risk assessment.
[0048] Figure 17 Prediction of hot section airfoil distortion as a result of service exposure using XactLIFE
[0049] Figure 18 Software process flow for real-time prognostics analysis of multiple turbine components DETAILED DESCRIPTION OF THE DRAWINGS
[0050] Referring to Figure 1, the prognostics system receives real time operating data (temperature, pressure, rpm, etc.), Figure 18, from pre-existing sensors and signal processing modules installed in the machinery under investigation 100;
the system as shown in Figure 2 analyses the engine mission data 100 using a rule based mission profile analyzer 102 to discern steady state and cyclic loads and the software program uses this thermal-mechanical loading information along with the basic engine design data to compute the pitch-line temperatures of the various stages of the hot gas path components; Figure 3 is the end result from the mission profile analyzer displaying cyclic load and temperature variability; this is followed by real-time temperature profile and non-linear finite element analysis 106 and the output of the FE analysis and operational data is used to trigger appropriate damage analysis algorithms to compute residual life of the components 101 as a function of real time engine usage.
the system as shown in Figure 2 analyses the engine mission data 100 using a rule based mission profile analyzer 102 to discern steady state and cyclic loads and the software program uses this thermal-mechanical loading information along with the basic engine design data to compute the pitch-line temperatures of the various stages of the hot gas path components; Figure 3 is the end result from the mission profile analyzer displaying cyclic load and temperature variability; this is followed by real-time temperature profile and non-linear finite element analysis 106 and the output of the FE analysis and operational data is used to trigger appropriate damage analysis algorithms to compute residual life of the components 101 as a function of real time engine usage.
[0051] Referring to Figure 4, the combustor model 103 computes discerns combustion conditions and creates temperature profile, Figure 5, either by using the semi-empirical relations or by computational fluid dynamics (CFD) based modeling techniques.
[0052] Referring to Figure 6, in the off-design engine modeling 104 the individual turbine and compressor have well defined operating characteristics in the form of a map that are used as input to the algorithm; the turbine map and compressor map are used as the basic input data in the Off-Design engine model; the algorithm finds an operating point on the compressor map and turbine map when the turbine engine is running at a given condition; this is followed by semi-empirical thermodynamic analysis, Figure 6, to compute the axial temperature as well as chord-wise temperature profiles of all hot gas path components; the thermodynamic module 105 is used to get certain node temperatures at the leading and trailing edges of the airfoil of the component; the flowchart in Figure 6 explains the process flow for engine temperature and Figure 7 shows the end result of the thermodynamic model computation 105 and this temperature profile data along with the mechanical loading data in terms of engine rotational speed and the pre-programmed finite element models of components are automatically fed as inputs to the non-linear FE
solver 106 to compute the combined thermal-mechanical stresses, plastic strains and temperatures that are operative at different nodes of the component finite element models.
solver 106 to compute the combined thermal-mechanical stresses, plastic strains and temperatures that are operative at different nodes of the component finite element models.
[0053] Referring to Figure 8-Figure 9, the individual nodal loading data is in turn automatically fed into the microstructure-based damage models 107 to compute the creep, low cycle fatigue and thermal-mechanical fatigue life of the individual nodes of a given component; This process determines the fracture critical locations within a given component and the remaining useful lives of hot gas path components as a function of engine usage 108.
[0054] Referring to Figure 10 and Figure 16, the probabilistic model is used for conducting microstructural variability based probabilistic life analysis and risk assessment 107 of the component and this is performed in various steps; in the first step the required distributions for a damage controlling microstructural variables are obtained and the three kinds of distributions used in the system are Normal, Weibull Figure 11, and Lognormal Figure 12; distribution bounds are defined by the user/data and this is followed by life calculations of major fracture critical locations using the distribution data; computing residual life for crack initiation, specific level of component distortion Figure 17 and crack growth based crack propagation and inspection intervals Figure 15 and Figure 16; the residual life distributions computed for the component are displayed as cumulative probability of failure versus residual life plots using lognormal and Weibull analysis techniques and these plots are used to quantify risk associated with following a specific life cycle management or usage strategy; Figure 14 is an example of the output of life results 108 displayed in the form of a histogram for different fracture critical locations of a component.
[0055] Referring to Figure 15 and Figure 16, safe inspection interval prediction based on crack growth analysis is used to quantify risks associated with the life cycle management strategy using specific non-destructive inspection techniques to inspect the components at overhaul or during field inspection and to recommend the frequency of inspection to minimize probability of failure during engine operation.
Claims (15)
1. A method and a system called XactLIFE for continuously monitoring variability of engine operating parameters and engine operating environment and predicting the usage and operating environment based life consumption and residual life of multiple components using standard data acquired from engine monitoring interfaces, comprising steps of: collecting and analyzing the data points (engine speed, TIT or EGT, ambient temperature and pressure and cooling airflow) acquired by each monitoring interface for assessing the type of loads (creep and/or fatigue) the monitored components are subjected to during service; continuously computing and quantifying the thermal-mechanical loads and quantifying the damage accumulated due to these loads using the physics of deformation and fracture processes operative in different components that allows the computation and identification of fracture critical location, estimation of life consumption and residual life of each component being monitored and fluctuations in specific component life parameters over time and continuously displaying the variability of fracture critical location and life for each component monitored; the methods followed in the proposed system are unique because the entire process of following different analytical techniques, related to different fields of gas turbine engineering, is conducted continuously in a logical sequence with the aid of appropriate graphical user interfaces and physics based modeling techniques. In addition, the uniqueness of the approach also lies in the use of physics based damage models as opposed to using empirical models, as is done by the OEM off-line, as a function of actual real-time engine usage, without using correlation coefficients or factors in the XactLIFE real-time system.
2. The method and the system as claimed in claim 1, further comprising a step of selecting a method of continuously computing variability of centrifugal loads and steady state as well as cyclic temperatures to establish the types of thermal-mechanical loads seen by the components using variability analysis, for each of the components monitored real-time.
3. The method and the system as claimed in claim 2, wherein the step of removing artifacts uses a rainflow analysis technique in combination with a homologous temperature plot to identify undesirable data points.
4. The method and the system as claimed in any of 1 to 3, further comprising a step of selecting a method of continuously computing variability of combustor liner temperature and combustor nozzle plane temperature profile as a function of engine usage real-time.
5. The method and the system as claimed in any of 1 to 4, further comprising a step of selecting a method of continuously computing variability of combustor liner temperature and combustor nozzle plane temperature profile as a function of engine usage real-time.
6. The method and the system as claimed in any of 1 to 5, further comprising a step of selecting a method of continuously computing variability of pitch-line temperature for different turbine gas path stages as a function of engine off-design usage conditions real-time.
7. The method and the system as claimed in any of 1 to 6, further comprising a step of selecting a thermodynamic modeling method of continuously computing variability of two dimensional temperature profiles for different turbine gas path stages as a function of engine usage conditions real-time.
8. The method and the system as claimed in any of 1 to 7, further comprising a step of selecting a heat transfer modeling method of continuously computing variability of temperature profiles for different turbine gas path stages as a function of engine usage conditions real-time.
9. The method and the system as claimed in any of 1 to 8, further comprising a step of selecting a heat transfer modeling method of continuously computing variability of temperature profiles for different turbine stages for non-gas path components as a function of engine usage conditions real-time.
10. The method and the system as claimed in any of 1 to 9, further comprising a step of selecting a non-linear finite element modeling based method of continuously computing variability of stress, strain and temperature profiles for different turbine components being monitored as a function of engine usage conditions real-time.
11. The method and the system as claimed in any of 1 to 10, further comprising a step of selecting physics based damage modeling method of continuously computing variability of distortion based fracture critical locations, life consumption, residual life and inspection interval for different turbine components being monitored as a function of engine usage conditions real-time.
12. The method and the system as claimed in any of 1 to 11, further comprising a step of selecting physics based damage modeling method of continuously computing variability of surface condition based fracture critical locations, life consumption, residual life and inspection interval for different turbine components being monitored as a function of engine usage conditions real-time.
13. The method and the system as claimed in any of 1 to 12, further comprising a step of selecting physics based damage and fracture modeling method of continuously computing variability of crack nucleation based fracture critical locations, life consumption and residual life for different turbine components being monitored as a function of engine usage conditions real-time.
14. The method and the system as claimed in any of 1 to 13, further comprising a step of selecting physics based fracture modeling method of continuously computing variability of crack propagation based fracture critical locations, life consumption, residual life and safe inspection intervals for different turbine components being monitored as a function of engine usage conditions real-time.
15. The method as claimed in any of 1 to 14, further comprising a step of selecting which of the variability of component parameters such as stress, strain and temperature profiles and the prognostics results such as the fracture critical location, surface condition, distortion, crack nucleation life and crack propagation life for which a representation of the variability is to be displayed.
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