CN102519726A - Acoustic-based diagnosis (ABD) method for compound fault of rolling bearing - Google Patents

Acoustic-based diagnosis (ABD) method for compound fault of rolling bearing Download PDF

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CN102519726A
CN102519726A CN2011104451406A CN201110445140A CN102519726A CN 102519726 A CN102519726 A CN 102519726A CN 2011104451406 A CN2011104451406 A CN 2011104451406A CN 201110445140 A CN201110445140 A CN 201110445140A CN 102519726 A CN102519726 A CN 102519726A
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signal
component
fault
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acoustic
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CN102519726B (en
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潘楠
伍星
迟毅林
刘畅
柳小勤
毛剑琳
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Kunming University Of Technology Asset Management Co ltd
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Abstract

The invention relates to an acoustic-based diagnosis (ABD) method for the compound fault of a rolling bearing, and belongs to the technical fields of mechanical equipment state monitoring and fault diagnosis. The method comprises the following steps of: spherizing an observed acoustic signal to form a signal space, performing dimension reduction by principal component analysis (PCA), and executing an efficient fast independent component analysis (EFICA) algorithm to obtain an independent component; separating normalized kurtosis to distinguish an impact component, a periodic component and Gaussian noise, forming a distance matrix by using envelope spectrum J-divergence, and performing fuzzy C-means clustering; preferably selecting the independent component by combining the kurtosis index of an impact signal to acquire an estimated signal, and performing morphology filtering; performing frequency domain sparse component analysis (SCA) on a signal which is instantaneously mixed approximately and has an obvious impact component, and thoroughly separating a compound fault signal; and analyzing the envelope demodulation spectrum of the separated signal, and performing fault diagnosis. Different from vibration monitoring in which a sensor is needed to be arranged on the surface of mechanical equipment, acoustic monitoring has the advantage that: the fault signal can be extracted from the received mechanical acoustic signal only by arranging a plurality of microphones at the periphery of the equipment.

Description

A kind of acoustics diagnose method to the rolling bearing combined failure
 
Technical field
Patent of the present invention relates to a kind of acoustics diagnose method to the rolling bearing combined failure, extracts, separates fault characteristic information through acoustic monitoring, belongs to plant equipment status monitoring and fault diagnosis technology field.
Background technology
Mechanical vibration are generation roots of mechanic sound signal, and the mechanic sound signal is the continuity that mechanical oscillation signal is propagated, and both are mutual unified integral body.When mechanical system such as rolling bearing or gear broke down, composition usually can appear significantly impacting in its characteristic signal, and acoustic characteristic also can change simultaneously, thereby contains status information of equipment.That acoustic measurement also has is harmless, noncontact, characteristics such as simple and easy to do, is not suitable for carrying out therefore that mechanic sound signal capable of using replaces vibration signal to carry out fault diagnosis under the situation of vibration monitoring at high temperature, the large complicated unit of high burn into etc.
According to statistics, nearly 30% mechanical fault all is to be caused by rolling bearing, and the quality of rolling bearing work will directly influence the duty of whole plant equipment.During the physical device operation, cause two or more rolling bearing combined failure coexistence through regular meeting.The signal that microphone receives is warp decay, coupling in transmission course; Contain in fault-signal wherein and very easily covered even flood and form typical convolution mixture model, thereby make the extraction of failure message more be similar to a blind deconvolution process by other undesired signals and outside noise.Because the microphone number is limited during on-the-spot test; And the existence of the numerous combined failures in addition of mechanical sound source number causes a lot of acoustical testings only to satisfy the owe fixed condition of observation signal number
Figure 2011104451406100002DEST_PATH_IMAGE001
less than source of trouble number
Figure 191922DEST_PATH_IMAGE002
.Need to suppress these interference and noise, in the hope of owing to extract mechanical fault signature under the fixed condition accurately, thereby carry out fault diagnosis. Summary of the invention
The present invention proposes a kind of from mechanical acoustical signal, extract bearing combined failure information owe to decide the blind deconvolution method; Can carry out under vibration monitoring and the situation of microphone number being not suitable for, utilize the mechanic sound signal to replace vibration signal to carry out that fault signature extracts and diagnosis less than source of trouble number.
Scheme of the present invention is: the acoustical signal of at first microphone being picked up is carried out the convolution nodularization, generates signal space, and then through PCA signal space is carried out dimensionality reduction, reduction higher-dimension signal subspace dimension.Carry out the EFICA algorithm subsequently, obtain isolated component.Then utilize the normalization kurtosis to distinguish and impact component, periodic component and Gaussian noise, adopt envelope spectrum J-divergence as between isolated component apart from the distance between hygrometer point counting amount, form distance matrix, eliminate the influence that time domain postpones.The matrix of adjusting the distance carries out fuzzy C-means clustering, the component that recognition feature is approximate.In conjunction with the preferred isolated component of impact signal kurtosis index; Obtain estimated signal; Estimated signal is carried out shape filtering; Get and be similar to instantaneous mixing after the filtering and impact the tangible signal of composition and carry out frequency domain SCA, the combined failure signal is thoroughly separated the envelope demodulation spectrum of final analysis separation signal.But thus just can be according to the just failure judgement source of trouble place that has that it's too late of known plant equipment components information.
Concrete steps of the present invention comprise as follows:
(1) initialization delay parameter
Figure 2011104451406100002DEST_PATH_IMAGE003
, the number of principal components and the number of clusters
Figure 2011104451406100002DEST_PATH_IMAGE005
;
The acoustical signal of (2) microphone being picked up
Figure 295193DEST_PATH_IMAGE006
is carried out centralization and is handled convolution nodularization observation signal
Figure 700812DEST_PATH_IMAGE006
one-tenth signal space
Figure 2011104451406100002DEST_PATH_IMAGE007
；
(3)
Figure 717309DEST_PATH_IMAGE007
carried out PCA processing and nodularization and (signal space is carried out dimensionality reduction; Reduction higher-dimension signal subspace dimension),
To
Figure 215287DEST_PATH_IMAGE007
; Data are carried out the EFICA algorithm, obtain isolated component
Figure 124523DEST_PATH_IMAGE008
;
(4) threshold value T is set; The normalization kurtosis
Figure DEST_PATH_IMAGE009
of calculating
Figure 7028DEST_PATH_IMAGE008
; When
Figure 297195DEST_PATH_IMAGE009
＜T, this component is designated as
Figure 928159DEST_PATH_IMAGE010
; As
Figure 366093DEST_PATH_IMAGE009
During>=T, this component is designated as
Figure DEST_PATH_IMAGE011
;
(5) the envelope spectrum J-divergence between calculating
Figure 724394DEST_PATH_IMAGE011
component; Form distance matrix
Figure 196963DEST_PATH_IMAGE012
,
Figure 231784DEST_PATH_IMAGE012
carried out Fuzzy C cluster (FCM)
Handle, thus the approximate component of recognition feature;
(6) if exists;
Figure 319006DEST_PATH_IMAGE011
in each polymeric type carried out preferably obtaining optimum component
Figure DEST_PATH_IMAGE013
;
If exists; Important addition in
Figure 618848DEST_PATH_IMAGE014
; Obtain estimated signal
Figure DEST_PATH_IMAGE015
; Form estimated signal
Figure 969058DEST_PATH_IMAGE016
at last; If
Figure 731478DEST_PATH_IMAGE010
do not exist, then final estimated signal ;
(7) estimated signal
Figure 100011DEST_PATH_IMAGE018
is carried out shape filtering; Get and be similar to instantaneous mixing after the filtering and impact the tangible signal of composition and carry out frequency domain SCA, the combined failure signal is thoroughly separated;
(8) envelope demodulation of analyzing separation signal is composed, and carries out fault judgement.
Advantage of the present invention and good effect: because the abundant information that contains of mechanical oscillation signal, physical significance is clear, and the magnitude variations scope is big, is convenient to identification and decision-making, so that the vibration diagnosis method becomes is the most frequently used at present, the most effective mechanical failure diagnostic method.Yet in the occasion that some vibration signal is difficult for picking up, (Acoustic-Based Diagnosis ABD) often shows its special advantages to the acoustics diagnose method.Therefore, enough get rid of or suppress interfering noise signal, extract echo signal to be identified in the acoustical signal of slave unit status information accurately, and then to carry out mechanical fault diagnosis be fully feasible.
Description of drawings
Rotating machinery fault simulated experiment platform and microphone position figure in Fig. 1 embodiment of the invention 1.
The time domain waveform of microphone pick-up of acoustic signals during the operation of Fig. 2 embodiment of the invention 1 rotating machinery fault simulated experiment platform.
The amplitude spectrum of microphone pick-up of acoustic signals during the operation of Fig. 3 embodiment of the invention 1 rotating machinery fault simulated experiment platform.
Fig. 4 embodiment of the invention 1 is improved the time domain waveform of block models blind deconvolution algorithm estimated signal.
Fig. 5 embodiment of the invention 1 is improved the envelope spectrum of block models blind deconvolution algorithm estimated signal.
The envelope spectrum of Fig. 6 embodiment of the invention 1 final separation signal.
The enforcement block diagram of Fig. 7 rolling bearing combined failure of the present invention acoustics diagnose method.
Embodiment
Below in conjunction with embodiment and accompanying drawing the present invention is done further elaboration, but protection content of the present invention is not limited to said scope.
Embodiment 1:
With certain whirling test stand centre bearer combined failure acoustics diagnose experiment is embodiment:
Fig. 1 representes the position relation of two microphones and testing table, and microphone all above 600mm, is to be the far sound field monitoring apart from combined failure bearing air line distance.Fault bearing correlation parameter is: pitch diameter
Figure 2011104451406100002DEST_PATH_IMAGE019
=39mm; Rolling body diameter
Figure 492947DEST_PATH_IMAGE020
=7.5mm; Rolling body number
Figure 2011104451406100002DEST_PATH_IMAGE021
=12, contact angle
Figure 81185DEST_PATH_IMAGE022
=0.The bearing inner race main axis rotation, the outer ring is fixed.Rotating speed is 800r/min; When being gyro frequency
Figure 2011104451406100002DEST_PATH_IMAGE023
for 13.33Hz; It is 64.61Hz that calculating can get the bearing outer ring fault characteristic frequency; The inner ring fault characteristic frequency is 95.38Hz, and the rolling body fault characteristic frequency is 5.38Hz.
Microphone picked up the time waveform and the amplitude spectrum of signal when Fig. 2 and Fig. 3 were the experiment table operation.Because the total system parts are more; Phase mutual interference between various acoustical signals during operation; Receive the reflex on wall and ground simultaneously; Cause the weak impact signal of bearing fault to be submerged in fully among the interference such as Gaussian noise and periodic signal, directly it is carried out Envelope Analysis and be difficult to obtain failure message accurately.
Fig. 4 and Fig. 5 are estimated signals after improving block models blind deconvolution algorithm.Because microphone distance test (DT) platform is far away; Initialization delay parameter
Figure 584979DEST_PATH_IMAGE003
=60; Principal component number
Figure 621068DEST_PATH_IMAGE004
=50; Because clusters number is if the energy of establishing that too much can cause disperses to make fault signature not obvious; So clusters number
Figure 434172DEST_PATH_IMAGE005
=3 is set; Set kurtosis coefficient threshold value =0.3, less than the periodic signal of thinking of this threshold value.Can find that from time domain waveform figure 1, No. 2 estimated signal impact characteristic is obvious, infer that it possibly be the impact composition that is produced by bearing fault.It is further verified as Envelope Analysis, from envelope spectrum, can obviously find out inside and outside circle through frequency and rolling body through frequency, but it is mingled in together, can't distinguish each other.
Fig. 6 uses shape filtering to remove unmodulated signal to two estimated signal among Fig. 4 and Fig. 5 to disturb; Carry out the net result of frequency domain SCA subsequently; Separation signal from Fig. 5 can clearly be seen spectral line and the frequency multiplication (130Hz, 195Hz etc.) thereof of 65Hz, meets the outer ring fault signature (64.7Hz) that calculates.From figure, can see the spectral line of 13Hz and 95Hz and the side frequency (like 108Hz) that the frequency multiplication both sides are spaced apart motor gyro frequency (13Hz) in the 3rd separation signal; Meet the inner ring fault signature (95.38Hz) that calculates, the outer ring, the inner ring fault signature is separated comes.Though the rolling body fault is faint, amplitude is less; But separation signal rolling body fault characteristic frequency (5Hz) and the side frequency composition that meets the rolling body fault signature still can be matched with the rolling body fault characteristic frequency (5.38Hz) that calculates by clear identification in Fig. 4.A little error is caused by frequency resolution
Figure 2011104451406100002DEST_PATH_IMAGE025
=1Hz.
Fig. 7 implements block diagram.Among the figure, the separation estimated signal that the two-way observation signal that is picked up by microphone finally obtains after the said flow process of implementation step is carried out Envelope Analysis to estimated signal, can carry out fault judgement.

Claims (2)

1. acoustics diagnose method to the rolling bearing combined failure, it is characterized in that: at first convolution nodularization observation acoustical signal becomes signal space, through PCA signal space is carried out dimensionality reduction; Carry out the EFICA algorithm subsequently, obtain isolated component; Utilize the normalization kurtosis to distinguish and impact component, periodic component and Gaussian noise; Then adopt envelope spectrum J-divergence as between isolated component apart from the distance between hygrometer point counting amount; Form distance matrix; And the matrix of adjusting the distance carries out fuzzy C-means clustering, in conjunction with the preferred isolated component of kurtosis index of impact signal, forms estimated signal with the periodic signal stack; Through shape filtering filtering unmodulated signal, estimated signal is done further separation at last, finally under the fixed condition rolling bearing combined failure information is extracted owing by the frequency-domain sparse component analysis.
2. according to the said mechanical fault acoustical signal of claim 1 method for distilling, its concrete steps comprise as follows:
(1) initialization delay parameter
Figure 205883DEST_PATH_IMAGE001
, the master number of the component
Figure 917487DEST_PATH_IMAGE002
and cluster number of ;
The acoustical signal of (2) microphone being picked up
Figure 108483DEST_PATH_IMAGE004
is carried out centralization and is handled convolution nodularization observation signal
Figure 691911DEST_PATH_IMAGE004
one-tenth signal space
Figure 879310DEST_PATH_IMAGE005
；
(3)
Figure 864584DEST_PATH_IMAGE005
being carried out PCA handles and nodularization; Obtain
Figure 862758DEST_PATH_IMAGE005
; Data
Figure 933482DEST_PATH_IMAGE005
are carried out the EFICA algorithm, obtain independent branch
Amount ;
(4) threshold value T is set; The normalization kurtosis
Figure 431962DEST_PATH_IMAGE007
of calculating
Figure 764352DEST_PATH_IMAGE006
; When ＜T, this component is designated as
Figure 581501DEST_PATH_IMAGE008
; As
Figure 213471DEST_PATH_IMAGE007
During>=T, this component is designated as ;
(5) the envelope spectrum J-divergence between calculating
Figure 598764DEST_PATH_IMAGE009
component; Form distance matrix
Figure 993973DEST_PATH_IMAGE010
; carried out the Fuzzy C clustering processing, from
And the approximate component of recognition feature;
(6) if
Figure 568491DEST_PATH_IMAGE009
exists;
Figure 835524DEST_PATH_IMAGE009
in each polymeric type carried out preferably obtaining optimum component
Figure 221375DEST_PATH_IMAGE011
;
If
Figure 624674DEST_PATH_IMAGE008
exists; Important addition in ; Obtain estimated signal ; Form estimated signal
Figure 312642DEST_PATH_IMAGE014
at last; If
Figure 524443DEST_PATH_IMAGE008
do not exist, then final estimated signal
Figure 954287DEST_PATH_IMAGE015
;
(7) estimated signal
Figure 930333DEST_PATH_IMAGE016
is carried out shape filtering; Get and be similar to instantaneous mixing after the filtering and impact the tangible signal of composition and carry out frequency domain SCA, the combined failure signal is thoroughly separated;
(8) envelope demodulation of analyzing separation signal is composed, and carries out fault judgement.
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Cited By (20)

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CN103149047A (en) * 2013-03-08 2013-06-12 沈阳化工大学 Cooling tower acoustic diagnosis method based on nonlinear mixed model
CN103839552A (en) * 2014-03-21 2014-06-04 浙江农林大学 Environmental noise identification method based on Kurt
CN103940597A (en) * 2014-04-08 2014-07-23 昆明理工大学 Method for detecting mechanical faults based on generalized extremum morphological filtering
CN104064186A (en) * 2014-06-26 2014-09-24 山东大学 Electrical equipment failure tone detection method based on independent component analysis
CN104568132A (en) * 2014-12-11 2015-04-29 昆明理工大学 Reference signal constraint-based mechanical characteristic acoustic signal frequency-domain semi-blind extraction method
CN105588720A (en) * 2015-12-15 2016-05-18 广州大学 Fault diagnosis device and method for antifriction bearing based on analysis on morphological component of acoustic signal
CN105659064A (en) * 2013-10-11 2016-06-08 斯奈克玛 Method, system and computer program for the acoustic analysis of a machine
CN106295688A (en) * 2016-08-02 2017-01-04 浙江工业大学 A kind of fuzzy clustering method based on sparse average
CN104198187B (en) * 2014-09-04 2017-04-12 昆明理工大学 Mechanical vibration fault characteristic time domain blind extraction method
CN107831013A (en) * 2017-10-11 2018-03-23 温州大学 A kind of Method for Bearing Fault Diagnosis for strengthening cyclic bispectrum using probability principal component analysis
CN108007689A (en) * 2017-12-06 2018-05-08 慈兴集团有限公司 A kind of bearing failure analysis method
CN108152037A (en) * 2017-11-09 2018-06-12 同济大学 Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering
CN108776801A (en) * 2018-04-17 2018-11-09 重庆大学 It is a kind of based on owing to determine the analog circuit fault features extracting method of blind source separating
CN109238728A (en) * 2018-09-12 2019-01-18 温州大学 The method and system of component failure diagnosis on a kind of vehicular engine
CN110598593A (en) * 2019-08-29 2019-12-20 东南大学 Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm
CN110926812A (en) * 2019-01-21 2020-03-27 北京化工大学 Rolling bearing single fault identification method based on acoustic emission
CN113074935A (en) * 2021-04-01 2021-07-06 西华大学 Acoustic separation and diagnosis method for impact fault characteristics of gearbox
CN114441174A (en) * 2022-02-09 2022-05-06 上海电气集团股份有限公司 Diagnosis method, system, equipment and medium for composite fault of rolling bearing
CN116520095A (en) * 2023-07-03 2023-08-01 昆明理工大学 Fault location method, system and computer readable storage medium
CN117723303A (en) * 2024-02-01 2024-03-19 湘潭大学 Acoustic monitoring method for wind generating set bearing

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CN103149047A (en) * 2013-03-08 2013-06-12 沈阳化工大学 Cooling tower acoustic diagnosis method based on nonlinear mixed model
CN105659064A (en) * 2013-10-11 2016-06-08 斯奈克玛 Method, system and computer program for the acoustic analysis of a machine
CN103839552A (en) * 2014-03-21 2014-06-04 浙江农林大学 Environmental noise identification method based on Kurt
CN103940597A (en) * 2014-04-08 2014-07-23 昆明理工大学 Method for detecting mechanical faults based on generalized extremum morphological filtering
CN104064186A (en) * 2014-06-26 2014-09-24 山东大学 Electrical equipment failure tone detection method based on independent component analysis
CN104198187B (en) * 2014-09-04 2017-04-12 昆明理工大学 Mechanical vibration fault characteristic time domain blind extraction method
CN104568132A (en) * 2014-12-11 2015-04-29 昆明理工大学 Reference signal constraint-based mechanical characteristic acoustic signal frequency-domain semi-blind extraction method
CN104568132B (en) * 2014-12-11 2017-04-12 昆明理工大学 Reference signal constraint-based mechanical characteristic acoustic signal frequency-domain semi-blind extraction method
CN105588720A (en) * 2015-12-15 2016-05-18 广州大学 Fault diagnosis device and method for antifriction bearing based on analysis on morphological component of acoustic signal
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CN108152037A (en) * 2017-11-09 2018-06-12 同济大学 Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering
CN108007689A (en) * 2017-12-06 2018-05-08 慈兴集团有限公司 A kind of bearing failure analysis method
CN108776801A (en) * 2018-04-17 2018-11-09 重庆大学 It is a kind of based on owing to determine the analog circuit fault features extracting method of blind source separating
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CN110926812A (en) * 2019-01-21 2020-03-27 北京化工大学 Rolling bearing single fault identification method based on acoustic emission
CN110598593B (en) * 2019-08-29 2022-03-25 东南大学 Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm
CN110598593A (en) * 2019-08-29 2019-12-20 东南大学 Planetary gearbox fault diagnosis method based on resonance sparse decomposition and FastICA algorithm
CN113074935A (en) * 2021-04-01 2021-07-06 西华大学 Acoustic separation and diagnosis method for impact fault characteristics of gearbox
CN114441174A (en) * 2022-02-09 2022-05-06 上海电气集团股份有限公司 Diagnosis method, system, equipment and medium for composite fault of rolling bearing
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CN117723303A (en) * 2024-02-01 2024-03-19 湘潭大学 Acoustic monitoring method for wind generating set bearing
CN117723303B (en) * 2024-02-01 2024-05-10 湘潭大学 Acoustic monitoring method for wind generating set bearing

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