CN109612732A - A kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum - Google Patents

A kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum Download PDF

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CN109612732A
CN109612732A CN201910069214.7A CN201910069214A CN109612732A CN 109612732 A CN109612732 A CN 109612732A CN 201910069214 A CN201910069214 A CN 201910069214A CN 109612732 A CN109612732 A CN 109612732A
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fault
signal
rolling bearing
envelope spectrum
failure
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周小龙
杨恭勇
姜振海
马风雷
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Beihua University
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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
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis

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Abstract

The invention discloses a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum, in the case of research Injured level, influence to rolling bearing working condition classification recognition result, rolling bearing rolling element fault-signal is analyzed, shaft revolving speed 1800r/min, sample frequency 12000Hz, degree of injury is divided into slightly by damage spot diameter, medium and severe, structure of the invention is scientific and reasonable, it is safe and convenient to use, this method carries out fault signature enhancing using MCKD algorithm to bearing vibration signal first, VMD decomposition is carried out to signal after enhancing, by being based on energy entropy production, kurtosis criterion chooses the IMF component comprising major failure characteristic information, envelope spectrum characteristic amplitude ratio and the envelope spectrum entropy of each sensitivity IMF component are extracted preferably to reflect and quantify failure spy Reference breath, and malfunction is identified using Fuzzy C-Means Cluster Algorithm, realize the diagnosis of rolling bearing working condition and fault type.

Description

A kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum
Technical field
The present invention relates to rolling bearing fault diagnosis art field, specially a kind of axis of rolling based on deconvolution and envelope spectrum Hold method for diagnosing faults.
Background technique
Rolling bearing is part the most commonly used in mechanical equipment, it provides secure support for mechanical structure, operation State will directly affect the operational safety and correlated performance of equipment, and since working environment is severe, rolling bearing is in morning duty cycle Phase easily generates the local defects failure such as abrasion, scratch and spot corrosion, however, its operating condition is complicated when mechanical equipment operates, generation Vibration signal is often mixed with strong external noise, and fault characteristic information is caused to flood wherein, not easy to identify and extraction, how strong The Weak fault feature that reflection rolling bearing is effectively extracted under background noise environment has become the hot spot of fault diagnosis field research And difficult point；
Bearing vibration signal have the characteristics that it is non-linear and non-stationary, for such signal, Time-Frequency Analysis Method tool There is preferably applicability, wherein with empirical mode decomposition (Empirical Mode Decomposition, EMD) and local mean value Being most widely used for (Local Mean Decomposition, LMD) is decomposed, but due to lacking solid theoretical basis and dividing It is limited when solution by factors such as noise, sample frequencys, there is serious end effect and modal overlap in EMD and LMD, in view of This, Dra, which is equal to 2014, proposes a kind of non-recursive signal decomposition method-variation mode decomposition (Variational Mode Decomposition, VMD), compared with EMD and LMD, VMD is substantially one group of adaptive wiener filter, is effectively solved with this Generated end effect and modal overlap problem due to envelope problem and recursive operation and in decomposable process, work as the axis of rolling When holding generation failure, vibration signal is multi -components modulated signal and fault characteristic information is faint, if directly carrying out to the signal VMD is decomposed, and certain intrinsic mode functions (Intrinsic Mode Function, IMF) component can be made affected by noise and feature It is unobvious, it is one to rolling bearing fault signal de-noising to enhance the effective signal-to-noise ratio of fault-signal, highlighting fault characteristic information A important process, the fault message of rolling bearing mainly exist with impulse form, maximal correlation kurtosis deconvolution (Maximum Correlated Kurtosis Deconvolution, MCKD) using related kurtosis as evaluation index, it is fully considering in signal On the basis of contained periodic impulse ingredient properties, deconvolution is realized by iterative process, can effectively protrude and be submerged in very noisy Continuous impulse is compared with minimum entropy deconvolution (Minimum Entropy Deconvolution, MED), has higher algorithm Shandong Stick and better fault signature reinforcing effect, the modulating characteristic of rolling bearing fault signal make its envelope intensively carry event Hinder information, therefore circuit envelope method is one of most effective diagnostic method, but traditional diagnostic method based on Envelope Analysis at present Mostly directly to observe envelope spectrum, failure is identified according to envelope spectrum peak and characteristic frequency, lacks dependent quantization parameter, Wu Fashi The automation of existing fault diagnosis, meanwhile, studies have shown that rolling bearing fault signal has " ambiguity of failure gradual change ", cause There are very strong similitude, common fault recognition method is difficult to carry out Precise Diagnosis to its fault type fault signature.
Summary of the invention
The present invention provides a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum, can effectively solve State the problem of proposing in background technique.
To achieve the above object, the invention provides the following technical scheme: a kind of axis of rolling based on deconvolution and envelope spectrum Method for diagnosing faults is held, is included the following steps:
S1, on rolling bearing fault simulated experiment platform by particular sample frequency acquire respectively normal condition, inner ring failure, Test data under outer ring failure and rolling element malfunction；
S2, the test data in step S1 carries out at noise reduction the vibration signal under four kinds of states using MCKD algorithm Reason, highlights the fault signature of signal；
S3, VMD decomposition is carried out after denoising to vibration signal in step S2, and is obtained by energy entropy production, kurtosis criterion Under different conditions can Efficient Characterization signal self-characteristic modal components；
S4, the envelope spectrum amplitude Characteristics ratio δ for calculating each modal componentsi、λiAnd normalization envelope spectrum entropy bi(i=1, 2 ..., K), and construct multiple features high dimensional feature vector
T=[δ1,···,δK,λ1,···,λK,b1,···,bK]；
S5, a part of data are chosen as master sample, take sample average as the initial cluster center of FCM algorithm, it is remaining Data are used as detection sample, and the working condition and fault type of rolling bearing are identified according to the cluster result of FCM.
According to the above technical scheme, using rolling acquired on rolling bearing fault simulated experiment platform in the step S1 Bearing is normal, the data under inner ring failure, outer ring failure and four kinds of operating conditions of rolling element failure are analyzed, and the experimental bench is by driving Motor, torque sensor, power meter and signal picker device composition, testing selected bearing designation is 6203 type deep-groove balls Bearing, in signal acquisition process, using B&K acquisition system, acceleration transducer type is 4507 type acceleration transducers, sampling Frequency is 12000Hz, and shaft revolving speed is 1800r/min.
According to the above technical scheme, pass through the analysis to a large amount of actual measurement bearing vibration signals, choosing in the step S5 Mode decomposition number K=5, punishment parameter α=2000 pair signal of rolling bearing is taken to decompose.
According to the above technical scheme, in the case of for research Injured level, to rolling bearing working condition Classification and Identification As a result rolling bearing rolling element fault-signal is analyzed in influence, shaft revolving speed 1800r/min, sample frequency 12000Hz, degree of injury are divided into slight, medium and severe injury by damage spot diameter, i.e., impaired loci be directly respectively 0.18mm, 0.36mm and 0.53mm, impaired loci depth are 0.27mm.
Compared with prior art, beneficial effects of the present invention: structure of the invention is scientific and reasonable, safe and convenient to use, the party Method carries out fault signature enhancing using MCKD algorithm to bearing vibration signal first, carries out VMD decomposition to signal after enhancing, By choosing the IMF component comprising major failure characteristic information based on energy entropy production, kurtosis criterion, extract sensitivity IMF points each The envelope spectrum characteristic amplitude ratio and envelope spectrum entropy of amount use fuzzy C-mean algorithm preferably to reflect and quantify fault characteristic information It clusters (Fuzzy C-Means Clustering, FCM) algorithm to identify malfunction, to realize rolling bearing working condition With the diagnosis of fault type, this method is by validity of the MCKD algorithm in terms of highlighting fault signature, VMD method in signal decomposition Accuracy and FCM algorithm of the superiority, Envelope Analysis of aspect in terms of fault message extraction in terms of failure modes can It is combined by property, passes through the analysis to rolling bearing measured data, the results showed that, mentioned method can effectively distinguish rolling bearing Working condition and fault type provide a kind of feasible method for the solution of problems.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention It applies example to be used to explain the present invention together, not be construed as limiting the invention.
In the accompanying drawings:
Fig. 1 is algorithm flow schematic diagram of the invention；
Fig. 2 is experimental provision schematic diagram of the invention；
Fig. 3 is rolling bearing structure parameter schematic diagram of the invention；
Fig. 4 is rolling element fault-signal time domain waveform schematic diagram of the invention；
Fig. 5 is rolling element fault-signal spectrum diagram of the invention；
Fig. 6 is fault-signal envelope spectrum schematic diagram of the invention；
Fig. 7 is time domain plethysmographic signal schematic diagram after MCKD noise reduction of the invention；
Fig. 8 is signal envelope spectrum schematic diagram after noise reduction of the invention；
Fig. 9 is VMD decomposition result schematic diagram after rolling element fault-signal MCKD noise reduction of the invention；
Figure 10 is the energy entropy production and kurtosis value schematic diagram of each IMF component of the invention；
Figure 11 is the envelope spectrum schematic diagram of sensitive component IMF1 of the invention；
Figure 12 is the envelope spectrum schematic diagram of sensitive component IMF2 of the invention；
Figure 13 is the envelope spectrum schematic diagram of sensitive component IMF5 of the invention；
Figure 14 is the rolling bearing fault recognition result schematic diagram of the invention based on MCKD and VMD envelope spectrum；
Figure 15 is the rolling bearing fault recognition result schematic diagram of the invention based on MCKD and EEMD envelope spectrum；
Figure 16 is the rolling bearing fault recognition result schematic diagram of the invention based on MCKD and LMD envelope spectrum；
Figure 17 is the initial cluster center schematic diagram of master sample in the case of Injured level of the invention；
Figure 18 is the rolling bearing fault recognition result schematic diagram in the case of Injured level of the invention.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
Embodiment: as shown in Figure 1, the present invention provides technical solution, a kind of rolling bearing based on deconvolution and envelope spectrum Method for diagnosing faults includes the following steps:
S1, on rolling bearing fault simulated experiment platform by particular sample frequency acquire respectively normal condition, inner ring failure, Test data under outer ring failure and rolling element malfunction；
S2, the test data in step S1 carries out at noise reduction the vibration signal under four kinds of states using MCKD algorithm Reason, highlights the fault signature of signal；
S3, VMD decomposition is carried out after denoising to vibration signal in step S2, and is obtained by energy entropy production, kurtosis criterion Under different conditions can Efficient Characterization signal self-characteristic modal components；
S4, the envelope spectrum amplitude Characteristics ratio δ for calculating each modal componentsi、λiAnd normalization envelope spectrum entropy bi(i=1, 2 ..., K), and construct multiple features high dimensional feature vector
T=[δ1,···,δK,λ1,···,λK,b1,···,bK]；
S5, a part of data are chosen as master sample, take sample average as the initial cluster center of FCM algorithm, it is remaining Data are used as detection sample, and the working condition and fault type of rolling bearing are identified according to the cluster result of FCM.
As shown in Fig. 2, according to the above technical scheme, using institute on rolling bearing fault simulated experiment platform in the step S1 The rolling bearing of acquisition is normal, the data under inner ring failure, outer ring failure and four kinds of operating conditions of rolling element failure are analyzed, the reality It tests platform to be made of driving motor, torque sensor, power meter and signal picker device, testing selected bearing designation is 6203 type deep groove ball bearings, in signal acquisition process, using B&K acquisition system, acceleration transducer type is that 4507 types accelerate Sensor, sample frequency 12000Hz are spent, shaft revolving speed is 1800r/min.
As seen in figures 3-6, according to the above technical scheme, bearing parameter such as Fig. 3 selects rolling element initial failure data to carry out Analysis, the failure are artificial pitting fault, and spark erosion technique is used to process lesion diameter on rolling element as 0.18mm, Lesion depths be 0.28mm single-point dent, data length 6000, time domain waveform as shown in figure 4, frequency spectrum as shown in figure 5, For effective observation signal frequency domain character, frequency spectrum takes 0 to 1000Hz.
As shown in Figure 4, due to noise jamming, though there are certain impact ingredients in the time domain waveform of fault-signal, it is advised Rule is not obvious, this shows that some smaller impact ingredients of energy are submerged in strong background noise in signal, by the bearing knot of table 3 The theory characteristic frequency that structure parameter can be calculated rolling element failure should be 119.6Hz.
However, there are multiple resonance bands in the spectrogram of Fig. 4, it is difficult to tell frequency relevant to failure in lower frequency region Spectrum peak spectral line carries out envelope spectrum analysis to the signal, is as a result shown in Fig. 6, only there is spectral line more outstanding in high frequency section Amplitude, but fault signature spectrum component is still difficult to find that.
As is seen in figs 7-10, according to the above technical scheme, noise reduction process is carried out to this signal using MCKD method, after noise reduction The time domain waveform and envelope spectrum of signal are as schemed, as shown in Figure 7, noise of the rolling element fault-signal after MCKD is denoised, in signal Ingredient is effectively suppressed, and the impact ingredient being submerged in noise originally is very prominent and regular more obvious, low in envelope spectrum Frequency impact ingredient more highlights, but there are still the influence of certain noise and other interference spectral lines, signal fault characteristic frequency is not yet It can accurately identify；
By the analysis to a large amount of actual measurement bearing vibration signals, mode decomposition number K=5 is chosen in step S5, is punished Penalty parameter α=2000 pair signal of rolling bearing is decomposed, and the VMD decomposition result of rolling element fault-signal is such as after MCKD enhances Shown in Figure 10.
As shown in figures 10-13, the energy entropy production and its kurtosis value of each IMF component are calculated separately, it can be seen that Tu10Zhong The energy entropy production and kurtosis value of IMF1, IMF2 and IMF5 are larger, and the above-mentioned 3 IMF components of selection are right as sense mode component It carries out the envelope spectrum that Hilbert demodulates to obtain each sensitivity IMF component as figs 11-13；
As figs 11-13, the envelope spectrogram of each modal components is relatively pure, can be clear at rolling element fault characteristic frequency The presence of peak value spectral line is seen on ground, shows that fault-signal after MCKD noise reduction, is increased in conjunction with VMD decomposition method and based on Energy-Entropy The sensitive IMF selection algorithm of amount-kurtosis criterion, can efficiently extract Rolling Bearing Fault Character, but by Figure 11 and Figure 12 It can be seen that still with the presence of the peak value spectral line of certain interference component in the envelope spectrum of IMF1 and IMF2, due to lacking dependent quantization ginseng Number, if Yi Zengjia fault identification mistake probability therefore need to be with only using single envelope spectrum peak registration as fault diagnosis foundation Signal envelope spectrum is foundation, extracts dependent quantization parameter to increase the reliability of fault diagnosis and realize its automation.
According to the above technical scheme, selecting normal rolling bearing and failure spot diameter is 0.18mm, and lesion depths are The data of inner ring failure, outer ring failure and rolling element failure are analyzed under the conditions of 0.28mm, randomly select 1800r/min revolving speed Under, each 40 groups of four kinds of status datas, data sampling point number takes 3000, and 20 groups of numbers are randomly selected in every kind of state sample data According to as master sample, remaining 20 groups as detection sample.
Signal is denoised first with MCKD, and signal after denoising is decomposed using VMD method, and in Whether frequency of heart, which close item principle occurs, determines default scale K, for the consistency for guaranteeing signal characteristic abstraction, by being based on energy The sensitive IMF selection algorithm of entropy production-kurtosis criterion seeks the envelope spectrum amplitude of 3 IMF components most sensitive for signal characteristic Value tag ratio and envelope spectrum entropy construct multiple features high dimensional feature vector T.
In feature vector calculating process, the higher-dimension for first solving each 20 master samples under 4 kinds of states of rolling bearing is special Vector is levied, amounts to 9 dimensional feature vectors of 80 samples, using sample average as the initial cluster center of FCM algorithm；Again to every kind The high dimensional feature vector of each 20 detections sample is solved under state, obtains 9 dimensional feature vectors of 80 detection samples, warp altogether The sample initial cluster center and part that obtain after MCKD noise reduction and VMD processing detect the following table of feature vector of sample:
Table 1
As shown in Table 1, there is biggish discrimination, and similar sample between the foreign peoples's sample handled through MCKD and VMD Clustering Effect between this is preferable, it is indicated above that using mentioned method solves herein above-mentioned parameter as feature vector to rolling The diagnosis of dynamic bearing working condition and fault type has preferable separability and diagnostic reliability.
As shown in figure 14, the classification recognition result that 80 detection samples are amounted under 4 kinds of states, weights in FCM algorithm and refers to Number m=2, iteration stopping threshold value are 10-6, and classification 1,2,3,4 respectively indicates rolling bearing normal condition, inner ring failure, outer in figure Enclose failure and rolling element failure；
As shown in figures 15-16, effect of the VMD method in terms of Rolling Bearing Fault Character extraction, to above-mentioned vibration signal Set empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD) is respectively adopted and LMD is carried out It decomposes, denoising and feature extraction is carried out to the bearing vibration signal under 4 kinds of states using treatment process, wherein sensitive IMF The 3 IMF components most sensitive to fault signature are chosen according to energy entropy production-kurtosis criterion, calculating decomposition obtains each quick Feel IMF component envelope spectrum amplitude Characteristics ratio and envelope spectrum entropy, form multiple features high dimensional feature vector, and using FCM algorithm into Row working condition and fault type recognition, recognition result are as shown in Figure 15 and Figure 16.
By Figure 15 and Figure 16 it is found that being decomposed using EEMD method to signal after, except normal condition test sample just Really identification is outer, and mistake all occurs in the recognition result of remaining state, and overall discrimination is 95%；
When using LMD decomposition method, the identification of normal condition and rolling element failure is correctly 100%, but inner ring failure and Also there is 1 group and 2 groups of erroneous judgements, overall discrimination 96.25% respectively in outer ring failure, though it is higher than EEMD method lower than VMD points The recognition result of solution method, it is possible thereby to verify, compared to EEMD and LMD decomposition method, the rolling bearing based on VMD envelope spectrum Method for diagnosing faults has higher diagnostic accuracy.
In the case of research Injured level, influence to rolling bearing working condition classification recognition result, using this Literary method analyzes rolling bearing rolling element fault-signal, wherein shaft revolving speed 1800r/min, sample frequency 12000Hz；
Degree of injury is divided into slight, medium and severe injury by damage spot diameter, i.e., impaired loci be directly respectively 0.18mm, 0.36mm and 0.53mm, impaired loci depth are 0.27mm.
As shown in figure 17, under the conditions of taking shaft revolving speed 1800r/min, normal, slight, medium and every kind of state of severe injury Each 30 groups of rolling element vibration data, data length 3000 carries out noise reduction process to signal using MCKD algorithm, passes through Energy-Entropy Increment-kurtosis criterion, which is chosen, decomposes the gained 3 IMF components most sensitive to fault signature through VMD, seeks each IMF component Envelope spectrum amplitude Characteristics are than completing the quantization to each component characterization value with envelope spectrum entropy, the envelope spectrum amplitude Characteristics that will acquire Than forming multiple features high dimensional feature vector T with envelope spectrum entropy, 20 feature vectors of every kind of state are randomly selected as standard sample This calculates master sample mean value as the initial cluster center in FCM algorithm, and remaining 10 as detection sample.
As shown in figure 18, under rolling bearing Injured level state, the variation tendency of initial cluster center it is more stable and Between foreign peoples's sample have biggish discrimination, while verifying proposed rolling bearing feature extracting method validity, also for The accuracy of consequent malfunction diagnosis provides guarantee, using FCM algorithm to 10 detection samples under every kind of state, amounts to 40 It is identified, in identification process, the Weighting exponent m of FCM is set as 2, and iteration stopping threshold value is 10-6, at MCKD and VMD The recognition result of obtained sample to be detected is managed, it is light to respectively indicate rolling bearing normal condition, rolling element for classification 1,2,3,4 in figure The detection sample standard deviation of micro-damage, rolling element moderate injury and rolling element severe injury, Injured level is correctly validated out, always Body is identified as 100%, so that verifying proposed method herein can be effectively used for the identification of rolling bearing Injured level state.
Compared with prior art, beneficial effects of the present invention: structure of the invention is scientific and reasonable, safe and convenient to use, the party Method carries out fault signature enhancing using MCKD algorithm to bearing vibration signal first, carries out VMD decomposition to signal after enhancing, By choosing the IMF component comprising major failure characteristic information based on energy entropy production, kurtosis criterion, extract sensitivity IMF points each The envelope spectrum characteristic amplitude ratio and envelope spectrum entropy of amount use fuzzy C-mean algorithm preferably to reflect and quantify fault characteristic information It clusters (Fuzzy C-Means Clustering, FCM) algorithm to identify malfunction, to realize rolling bearing working condition With the diagnosis of fault type, this method is by validity of the MCKD algorithm in terms of highlighting fault signature, VMD method in signal decomposition Accuracy and FCM algorithm of the superiority, Envelope Analysis of aspect in terms of fault message extraction in terms of failure modes can It is combined by property, passes through the analysis to rolling bearing measured data, the results showed that, mentioned method can effectively distinguish rolling bearing Working condition and fault type provide a kind of feasible method for the solution of problems.
Finally, it should be noted that being not intended to restrict the invention the foregoing is merely preferred embodiment of the invention, to the greatest extent Present invention has been described in detail with reference to the aforementioned embodiments for pipe, for those skilled in the art, still can be with It modifies the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features.It is all Within the spirit and principles in the present invention, any modification, equivalent replacement, improvement and so on should be included in guarantor of the invention Within the scope of shield.

Claims (4)

1. a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum, characterized by the following steps:
S1, by particular sample frequency normal condition, inner ring failure, outer ring are acquired respectively on rolling bearing fault simulated experiment platform Test data under failure and rolling element malfunction；
S2, the test data in step S1 is subjected to noise reduction process to the vibration signal under four kinds of states using MCKD algorithm, dashed forward The fault signature of display signals；
S3, VMD decomposition is carried out after denoising to vibration signal in step S2, and difference is obtained by energy entropy production, kurtosis criterion Under state can Efficient Characterization signal self-characteristic modal components；
S4, the envelope spectrum amplitude Characteristics ratio δ for calculating each modal componentsi、λiAnd normalization envelope spectrum entropy bi(i=1,2 ..., K), And construct multiple features high dimensional feature vector
T=[δ1,…,δK,λ1,…,λK,b1,…,bK]；
S5, a part of data are chosen as master sample, take sample average as the initial cluster center of FCM algorithm, remaining data As detection sample, and identify according to the cluster result of FCM the working condition and fault type of rolling bearing.
2. a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum according to claim 1, feature Be, in the step S1 using acquired rolling bearing on rolling bearing fault simulated experiment platform normal, inner ring failure, outer Data under circle four kinds of operating conditions of failure and rolling element failure are analyzed, and the experimental bench is by driving motor, torque sensor, power Meter and signal picker device composition, testing selected bearing designation is 6203 type deep groove ball bearings, in signal acquisition process, Using B&K acquisition system, acceleration transducer type is 4507 type acceleration transducers, sample frequency 12000Hz, shaft turn Speed is 1800r/min.
3. a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum according to claim 1, feature It is, by the analysis to a large amount of actual measurement bearing vibration signals in the step S5, chooses mode decomposition number K=5, punishes Penalty parameter α=2000 pair signal of rolling bearing is decomposed.
4. a kind of Fault Diagnosis of Roller Bearings based on deconvolution and envelope spectrum according to claim 1, feature It is, in the case of research Injured level, influence to rolling bearing working condition classification recognition result, to rolling bearing Rolling element fault-signal is analyzed, and shaft revolving speed 1800r/min, sample frequency 12000Hz, degree of injury is by damage spot diameter It is divided into slight, medium and severe injury, i.e. impaired loci is directly respectively 0.18mm, 0.36mm and 0.53mm, and impaired loci depth is 0.27mm。
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Application publication date: 20190412