CN101701938B - Audio detection method of defect type of workpiece adopting vibrating-mode frequency combination - Google Patents
Audio detection method of defect type of workpiece adopting vibrating-mode frequency combination Download PDFInfo
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Abstract
The invention relates to an audio detection method of the defect type of a workpiece adopting vibrating-mode frequency combination. The existing audio detecting technique has low efficiency and low accuracy on detecting the defects of complex workpieces. The method of the invention has the following steps: firstly, carrying out audio detection on the workpiece to obtain the optimal sound clip; secondly, extracting characteristic parameters from the optimal sound clip; thirdly, establishing the corresponding relationship between a vibrating-mode frequency combined matrix and the defect type by applying mode identification; and finally, utilizing the completed corresponding relationship between the vibrating-mode frequency combined matrix and the defect type to detect the defect of the workpiece. The method adopts a vibrating-mode frequency combinational vector formed by the difference between a first-order or multi-order vibrating-mode frequency and the vibrating-mode frequency in the frequency spectrum of the workpiece to detect the defect type of the workpiece, thereby solving the problem whether the workpiece is qualified or not and distinguishing the defect type.
Description
Technical field
The invention belongs to the audio detection field, be specifically related to a kind of audio detection method of defect type of workpiece that adopts vibration shape combination of frequency.
Background technology
Audio detection is that a kind of audio frequency characteristics during according to Workpiece vibration comes a kind of lossless detection method that its internal soundness is detected, and with respect to X ray, ultrasonic etc., its cost is low, easy to operate.It is used for workpiece or the whether qualified judgement of other products as a kind of qualitative detection, utilizes funtcional relationship between resonant frequency and the ball milling rate as the detection of automobile camshaft ball milling rate; The measurement of body chambervolume utilizes the relation between resonant frequency and the volume to obtain the respective function relation by linear regression; The qualified egg of frequency spectrum that birds, beasts and eggs shell crack detection and classified use egg knock sound has a tangible predominant frequency, and the predominant frequency value is lower, has the magnetic shoe predominant frequency value of crackle higher relatively or have the big frequency of a plurality of amplitudes not have the predominant frequency value; The detection of bearing crack defect utilizes the audio signal frequency spectrum energy distribution characteristics of bearing exciting, comprises more radio-frequency component in the frequency spectrum of qualified bearing, and the radio-frequency component that the defectiveness bearing comprises is less.
Usually above-mentioned audio detection adopts single characteristic quantity such as natural frequency or attenuation rate, tested workpiece regular shape such as spheroid, cube, right cylinder etc. usually.For baroque workpiece, because of the complicacy of its vibration, single characteristic quantity is difficult to the dissimilar damage of complete description workpiece.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, a kind of audio detection method of defect type of workpiece that adopts vibration shape combination of frequency is provided.It is that characteristic parameter constitutes incompatible judgement workpiece, defect of vibration shape group of frequencies and type thereof that this method is utilized the single order of workpiece or multistage vibration shape frequency and vibration shape frequency-splitting thereof.
The concrete steps of the inventive method are:
Step (1), workpiece is carried out audio-frequency test, obtain best sound clip.Concrete grammar is:
A, form voice acquisition system by microphone and taping tool, microphone is placed around the exciting piece, workpiece from the steady exciting piece that freely drops to of level altitude 10mm-15mm, is gathered sound and saved as audio files by voice collection device, and each workpiece is preserved an audio files separately.
B, the audio files that collects is imported in the signal processing software, extract best sound clip and promptly drop to the sound clip that the exciting BOB(beginning of block) finishes to vibration from workpiece.The method of the best sound clip of described extraction is: workpiece falls magnitude of sound and is far longer than the noise amplitude in time domain, the threshold value that sound and noise are fallen in definition difference is M (definite according to the actual size that falls sound and noise), search for amplitude in chronological order, is starting point with magnitude of sound greater than the time point of threshold point, count t time point (time point length is fallen the time span decision of free vibration by workpiece) backward, this time period is best sound clip.
Step (2), from best sound clip, extract characteristic parameter.Concrete grammar is: use Fourier function (fft function) in signal processing software best sound clip is done spectrum transformation, the typical crest frequency that extracts on the frequency spectrum is a vibration shape natural frequency, and calculate frequency-splitting between the adjacent vibration shape, with vibration shape natural frequency and frequency-splitting vibration shape combination of frequency vector as workpiece.
Choose each 30～50 of various classification defective workpiece and repeat above-mentioned steps (1) and step (2), obtain workpiece vibration shape combination of frequency matrix.
The corresponding relation between vibration shape combination of frequency matrix and the defect type is set up in step (3), application model identification.
Step (4), utilize the corresponding relation between completed vibration shape combination of frequency matrix and the defect type to detect workpiece, defect.Concrete grammar is:
Allow a workpiece from the steady exciting piece that freely drops to of level altitude 10mm-15mm, gather sound and save as audio files by voice collection device, the lead-in signal process software extracts best sound clip, repeating step (2) obtains the vibration shape combination of frequency vector of this workpiece, search classification under the vibration shape combination of frequency vector of this workpiece at the corresponding relation between vibration shape combination of frequency matrix and the defect type, finish the judgement of defect type of workpiece.
The present invention adopts single order or the formation of the difference between multistage vibration shape frequency and the vibration shape frequency vibration shape combination of frequency vector in the workpiece frequency spectrum to detect defect type of workpiece.Compare with the conventional audio detection method, this method has solved that conventional audio detected characteristics amount is single, the limitation of measured workpiece regular shape, is the expansion that conventional audio is detected, and not only can solve the whether qualified of workpiece, also can distinguish defect type.
Embodiment
Case study on implementation one, magnetic shoe defective audio detection
Magnetic shoe is the magnetic work of a kind of dome-shaped watt shape, and global density is inhomogeneous, low both sides height in the middle of presenting.As the main accessory of various motors, magnetic shoe is widely used in industries such as information industry, motorcycle, electric tool industry, auto industry, medical treatment, mine metallurgy, industrial automation control, petroleum-based energy, civilian industry.Owing to its shape and process for machining and manufacturing reason, be prone to various crackles and fall piece, and inner naked eyes are invisible that dark quit a post and thus leave it vacant falls into.Magnetic shoe adopts the manual detection defective at present, and detection efficiency is low, defective false drop rate height.
At model 5479 magnetic shoes, determined that by the dye penetrating detection defect type of magnetic shoe finished product is:
AIO---axially run through and deeply be parallel to the short crack that the bottom surface is positioned at the centre
TI---intrados tangentially is parallel to the crackle of end face
TIO---axially run through the short crack that deeply is parallel to the bottom surface
AI---axially parallel promptly is positioned at the axial crack of intrados in the intrados crackle of bottom surface
AO---axially parallel promptly is positioned at the axial crack of extrados in the extrados crackle of bottom surface
Its concrete implementation step is as follows:
Step (1), magnetic shoe is carried out audio-frequency test, obtain best sound clip.Concrete grammar is:
A, carry phonographic recorder by microphone and computing machine and form voice collection device, microphone is placed by the exciting piece, open behind the phonographic recorder magnetic shoe from the steady exciting piece that freely drops to of level altitude 10mm, gather sound and save as the audio files that sample frequency is 48kHz, each magnetic shoe workpiece is preserved an audio files separately.
B, the voice signal that collects is imported in the matlab7.0 software, extract best sound clip and promptly drop to the sound that the exciting BOB(beginning of block) finishes to vibration from magnetic shoe.Concrete grammar is: magnetic shoe falls magnitude of sound and is far longer than the noise amplitude in time domain, according to falling the threshold value that sound and the actual strength of noise definition difference fall with noise is 0.6, search for amplitude in chronological order, is starting point with magnitude of sound greater than the time point of threshold point, count 8000 time points backward, this time period is best sound clip.
Step (2), from best sound clip, extract characteristic parameter.Concrete grammar is: the fft function of using in the matlab software is done spectrum transformation to best sound clip, the typical crest frequency of searching on the frequency spectrum is a vibration shape frequency, magnetic shoe has 3 typical peak values to be respectively 9.6kHz section, 15.6kHz section, 20kHz section on frequency spectrum, be defined as the 7th first order mode frequency, the 8th first order mode frequency, the 9th first order mode frequency respectively, extract this 3 first order mode frequency, calculate the frequency-splitting and the 8th between the 7th, 8 first order modes, the frequency-splitting between 9 first order modes, with the vibration shape combination of frequency vector of these 5 values as magnetic shoe.
Choose each 30-50 of defective workpiece of all categories and repeat above-mentioned steps (1) and step (2), obtain workpiece vibration shape combination of frequency matrix.
The corresponding relation between vibration shape combination of frequency matrix and the defect type is set up in step (3), application model identification.Concrete grammar is:
C, the qualitative characteristics and the vibration shape frequency matrix of magnetic shoe 5479 contrasted, at first observe the 7th first order mode frequency, with 9.7kHz is threshold value, less than 9.7kHz be serious AIO class of defective and TIO class defective, greater than 9.7kHz for the AI/AO class and do not have the magnetic shoe of defective.
D, on the 7th rank basis of classification, observe the 8th first order mode frequency, when the 7th first order mode frequency during, be threshold value with 15.3kHz less than 9.7kHz, less than 15.3kHz be AIO class defective, greater than 15.2kHz but less than 16kHz is TIO class defective.Observe the relation between the 9th rank, the 7th, 8 jump values, the 8th, 9 jump values and the magnetic shoe qualitative characteristics again, TIO class defective the 7th, 8 vibration shape frequency-splittings are greater than 6.2kHz.
In e, the magnetic shoe of the 7th first order mode frequency greater than 9.7kHz, AO/AI class magnetic shoe the 7th first order mode frequency is observed the 8th first order mode frequency less than 10kHz, is threshold value with 16kHz, less than 16kHz but greater than 15.3kHz be AI/AO class defective magnetic shoe, greater than 16kHz for there not being defective class magnetic shoe.Observe the relation between the 9th rank, the 7th, 8 jump values, the 8th, 9 jump values and the magnetic shoe qualitative characteristics again, magnetic shoe the 9th first order mode frequency that does not have the defective class is greater than 21kHz.
Above-mentioned pattern recognition classifier result is gathered as can be known:
The vibration shape combination of frequency of AIO class defective is:
F7＜9.7kHz，F8＜14kHz，F8-F7＜5.3kHz
F7＜9.7kHz，14kHz＜F8＜15.3kHz，5.2kHz＜F8-F7＜5.8kHz
The vibration shape combination of frequency of TIO class defective is:
F7＜9.7kHz，15.2kHz＜F8＜16kHz，F8-F7＞6.2kHz
The vibration shape combination of frequency of AI/AO class defective is:
9.6kHz＜F7＜10kHz.15.3kHz＜F8＜16kHz
The vibration shape combination of frequency of O class defective is:
F7＞9.7kHz，F8＞16kHz，F9＞21kHz
Step (4), utilize the corresponding relation between completed vibration shape combination of frequency matrix and the defect type to detect workpiece, defect.Concrete grammar is:
Selecting any model is that 5479 magnetic shoe steadily drops to the exciting piece by the audio detection step by level altitude 10mm freedom, pick up sound by the sound pick device, gather sound and save as the audio files that sample frequency is 48kHz, import to matbab software and extract best sound clip, repeating step (2) obtains the vibration shape combination of frequency vector of this workpiece, the 7th first order mode frequency is 9.5859kHz, the 8th first order mode frequency is 12.7031kHz, the 9th first order mode frequency is 20.2535kHz, calculate adjacent vibration shape natural frequency difference and get the 7th, 8 first order mode frequency-splittings are 11.1111kHz, the 8th, 9 first order mode frequency-splittings are 7.5504kHz, search classification under the vibration shape combination of frequency vector of this workpiece at the corresponding relation between vibration shape combination of frequency matrix and the defect type, this magnetic shoe has AIO class defective promptly axially to run through deeply to be parallel to the short crack of bottom surface in the middle of being positioned at as can be known, adopts the infiltration detection method to detect its surface imperfection and can see an axial crack that runs through internal and external cambered surface in the middle of magnetic shoe.
Case study on implementation two, magnet ring defective audio detection
Magnet ring is the cylinder magnetic material of a kind of xsect for annular, and the defective of general magnet ring is generally annular surface upper edge radial cracking, and part extends on the cylinder, and naked eyes generally are difficult for seeing.
Its concrete implementation step is as follows:
Step (1), magnet ring is carried out audio-frequency test, obtain best sound clip.Concrete grammar is:
A, form voice collection device by microphone and phonetic codec chip, microphone is placed by the exciting piece, magnet ring one end is leaned against on the exciting piece, the other end is from the steady exciting piece that freely drops to of level altitude 10mm, gather sound and save as the audio files that sample frequency is 48kHz, each workpiece is preserved an audio files separately.
B, the voice signal that collects is imported in the matlab7.0 software, extract best sound clip and promptly drop to the exciting BOB(beginning of block) and finish to vibration from magnetic shoe.Concrete grammar is: magnetic shoe falls magnitude of sound and is far longer than the noise amplitude in time domain, the threshold value that sound and noise are fallen in definition difference is 0.6, sequential search amplitude on time domain, is starting point with magnitude of sound greater than the time point of threshold point, count 10240 time points backward, this time period is best sound clip.
Step (2), from best sound clip, extract characteristic parameter.Concrete grammar is: the fft function of using in the matlab software is done spectrum transformation to best sound clip, the typical crest frequency of searching on the frequency spectrum is a vibration shape frequency, magnet ring has only a typical peaks that is positioned at the 16.9kHz section, the vibration shape natural frequency that is magnet ring has only single order, extracts the vibration shape combination of frequency vector of the vibration shape natural frequency on these rank as magnet ring.Choose qualified and have each 50 of the magnet rings of crack defect to repeat above-mentioned steps (1) and step (2), obtain magnet ring vibration shape combination of frequency matrix.
The corresponding relation between vibration shape frequency matrix and the defect type is set up in step (3), application model identification.Concrete grammar is:
Because magnet ring has only a first order mode frequency, the qualitative characteristics of magnet ring and the vibration shape characteristic quantity of magnet ring are contrasted, confirm that through repetition test the whether qualified threshold value of difference magnet ring is 16.9kHz.
Step (4), utilize the corresponding relation between completed vibration shape combination of frequency matrix and the defect type to detect workpiece, defect.Concrete grammar is:
Select an any magnet ring one end to lean against on the exciting piece, the other end steadily drops to the exciting piece from level altitude 10mm, pick up sound by the sound pick device, saving as sample frequency is the wav formatted file of 48kHz, import to mat lab software and extract best sound clip, repeating step (2) obtains the vibration shape combination of frequency vector of this workpiece, vibration shape frequency is 16.7kHz, search classification under the vibration shape combination of frequency vector of this workpiece at the corresponding relation between vibration shape combination of frequency matrix and the defect type, this magnet ring is defective magnet ring as can be known.
Claims (1)
1. an audio detection method of defect type of workpiece that adopts vibration shape combination of frequency is characterized in that this method comprises the steps:
Step (1), workpiece is carried out audio-frequency test, obtain best sound clip, concrete grammar is:
A, form voice acquisition system by microphone and taping tool, microphone is placed around the exciting piece, workpiece from the steady exciting piece that freely drops to of level altitude 10mm～15mm, is gathered sound and saved as audio files by voice collection device, and each workpiece is preserved an audio files separately;
B, the audio files that collects is imported in the signal processing software, extract best sound clip and promptly drop to the sound clip that the exciting BOB(beginning of block) finishes to vibration from workpiece; The method of the best sound clip of described extraction is: workpiece falls magnitude of sound and is far longer than the noise amplitude in time domain, the threshold value that sound and noise are fallen in definition difference is M, search for amplitude in chronological order, is starting point with magnitude of sound greater than the time point of threshold point, count t time point backward, this time period is best sound clip;
Step (2), from best sound clip, extract characteristic parameter, concrete grammar is: use the Fourier function in signal processing software best sound clip is done spectrum transformation, the typical crest frequency that extracts on the frequency spectrum is a vibration shape natural frequency, and calculate frequency-splitting between the adjacent vibration shape, with vibration shape natural frequency and frequency-splitting vibration shape combination of frequency vector as workpiece;
Choose each 30～50 of various classification defective workpiece and repeat above-mentioned steps (1) and step (2), obtain workpiece vibration shape combination of frequency matrix;
The corresponding relation between vibration shape combination of frequency matrix and the defect type is set up in step (3), application model identification;
Step (4), utilize the corresponding relation between completed vibration shape combination of frequency matrix and the defect type to detect workpiece, defect, concrete grammar is:
Allow a workpiece from the steady exciting piece that freely drops to of level altitude 10mm～15mm, gather sound and save as audio files by voice collection device, the lead-in signal process software extracts best sound clip, repeating step (2) obtains the vibration shape combination of frequency vector of this workpiece, search classification under the vibration shape combination of frequency vector of this workpiece at the corresponding relation between vibration shape combination of frequency matrix and the defect type, finish the judgement of defect type of workpiece.
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| CN2009101546765A CN101701938B (en) | 2009-11-23 | 2009-11-23 | Audio detection method of defect type of workpiece adopting vibrating-mode frequency combination |
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| CN102393397B (en) * | 2011-08-30 | 2014-04-09 | 成都四星液压制造有限公司 | System and method for detecting surface defects of magnetic shoe |
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| CN111601225B (en) * | 2020-05-11 | 2021-09-03 | 江西立讯智造有限公司 | Bluetooth headset acoustics testing arrangement |
| CN113588782A (en) * | 2021-07-01 | 2021-11-02 | 广州珠江天然气发电有限公司 | Nondestructive inspection method, device and equipment based on audio analysis |
| CN114544772B (en) * | 2022-04-26 | 2022-08-05 | 华南农业大学 | Device and method for detecting duck egg cracks based on convolutional neural network and voice frequency spectrum |
| CN115166049B (en) * | 2022-09-07 | 2022-12-02 | 广东工业大学 | Laser ultrasonic real-time detection system and method based on additive manufacturing |
| CN117647583A (en) * | 2024-01-29 | 2024-03-05 | 中国林业科学研究院木材工业研究所 | Method and device for on-line quality inspection of artificial board and decorative artificial board |
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