CN106404396A - Rolling bearing fault diagnosis method - Google Patents
Rolling bearing fault diagnosis method Download PDFInfo
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- CN106404396A CN106404396A CN201610780491.5A CN201610780491A CN106404396A CN 106404396 A CN106404396 A CN 106404396A CN 201610780491 A CN201610780491 A CN 201610780491A CN 106404396 A CN106404396 A CN 106404396A
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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 rolling bearing fault diagnosis method comprising the steps that the vibration signal of a rolling bearing is acquired; signal decomposition is performed on the vibration signal by using an EMD empirical mode decomposition algorithm so that decomposition signals are obtained; peak extraction, envelope detection and time and frequency domain transformation are performed on the decomposition signals by using PeakVue so that spectrum signals are obtained; the fault frequency of the rolling bearing is calculated; and the spectrum signals and the fault frequency are contrasted so that the fault diagnosis result of the rolling bearing can be obtained. Therefore, fault diagnosis of the rolling bearing can be realized through combination of the EMD empirical mode decomposition algorithm and the PeakVue method; furthermore, the method can be completed online so that equipment is ensured to safely and efficiently operate for a long time.
Description
Technical field
The present invention relates to rotary machinery fault diagnosis technical field, more particularly to a kind of fault diagnosis side of rolling bearing
Method.
Background technology
Rolling bearing is widely used in industrial system, and as the critical component of rotating machinery, its running status decides
The performance of whole system, according to incompletely statistics, leads to rotating machinery to there are about 30% the reason breaking down and is because rolling bearing
Break down.
As can be seen here, how to realize the fault diagnosis to rolling bearing being capable of safe and efficient long-term fortune to ensure equipment
Row is those skilled in the art ground urgently to be resolved hurrily problem.
Content of the invention
It is an object of the invention to provide a kind of method for diagnosing faults of rolling bearing, for realizing the fault to rolling bearing
Diagnosis is to ensure that equipment being capable of safe and efficient longtime running.
For solving above-mentioned technical problem, the present invention provides a kind of method for diagnosing faults of rolling bearing, including：
Obtain the vibration signal of rolling bearing；
Using EMD empirical mode decomposition algorithm, described vibration signal is carried out signal decomposition and obtain decomposed signal；
Being converted to frequently of peak extraction, envelope detection and time-frequency domain is carried out using PeakVue to described decomposed signal
Spectrum signal；
Calculate the failure-frequency of described rolling bearing；
Described spectrum signal and described failure-frequency are carried out contrasting the fault diagnosis result obtaining described rolling bearing.
Preferably, using EMD empirical mode decomposition algorithm, described vibration signal is carried out signal decomposition and divided described
Also include before solution signal：Described vibration signal is filtered；
Wherein, by high pass filter, described vibration signal is filtered.
Preferably, the cut-off frequency of described high pass filter is 3-4 times of the peak frequency of described vibration signal.
Preferably, described vibration signal is vibration acceleration signal；
Wherein, described vibration acceleration signal is obtained by vibration acceleration sensor.
Preferably, described peak extraction, envelope detection and time domain and frequency domain are carried out to described decomposed signal using PeakVue
The spectrum signal that is converted to specifically include：
Peak extraction is carried out to described decomposed signal with predetermined sampling time interval and obtains peak extraction signal；
Carry out process using peak extraction signal described in Hilbert transform pairs and obtain envelope signal；
Using FFT, line translation is entered to described envelope signal and obtain described spectrum signal.
Preferably, described predetermined sampling time interval is the inverse of 2.56 times of the peak frequency of described vibration signal.
Preferably, described failure-frequency specifically include outer ring failure-frequency, inner ring failure-frequency, rolling element failure-frequency or
Retainer failure-frequency.
Preferably, described described spectrum signal and described failure-frequency are carried out contrasting the fault obtaining described rolling bearing
Diagnostic result is specially：
Obtain spectrogram using described spectrum signal；
Judge whether the frequency range in described spectrogram comprises described outer ring failure-frequency, inner ring failure-frequency, rolling
Body failure-frequency, retainer failure-frequency and respective integer multiple frequency；
If it is, output fault cues information；
If it is not, then output regular prompt information；
Wherein, described fault diagnosis result includes described fault cues information and described regular prompt information.
The method for diagnosing faults of rolling bearing provided by the present invention, including the vibration signal obtaining rolling bearing；Using
Vibration signal is carried out signal decomposition and obtains decomposed signal by EMD empirical mode decomposition algorithm；Using PeakVue, decomposed signal is entered
Row peak extraction, envelope detection and time-frequency domain be converted to spectrum signal；Calculate the failure-frequency of rolling bearing；By frequency spectrum
Signal and failure-frequency carry out contrasting the fault diagnosis result obtaining rolling bearing.As can be seen here, by using EMD empirical modal
Decomposition algorithm is combined with PeakVue method and can realize the fault diagnosis to rolling bearing it is even more important that the method is permissible
Complete online it is ensured that equipment being capable of safe and efficient longtime running.
Brief description
In order to be illustrated more clearly that the embodiment of the present invention, the accompanying drawing of use required in embodiment will be done simply below
Introduce it should be apparent that, drawings in the following description are only some embodiments of the present invention, for ordinary skill people
For member, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.
A kind of flow chart of the method for diagnosing faults of rolling bearing that Fig. 1 provides for the present invention；
Fig. 2 is before vibration signal provided in an embodiment of the present invention filters and filtered time domain beamformer；
Fig. 3 is that EMD provided in an embodiment of the present invention decomposes, the time domain waveform of the first component after decomposition and second component
Figure；
Time domain beamformer before Fig. 4 is the first component peak extraction provided in an embodiment of the present invention and after peak extraction；
Fig. 5 is the PeakVue spectrogram of the first component provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on this
Embodiment in invention, under the premise of not making creative work, obtained is every other for those of ordinary skill in the art
Embodiment, broadly falls into the scope of the present invention.
The core of the present invention is to provide a kind of method for diagnosing faults of rolling bearing.
In order that those skilled in the art more fully understand the present invention program, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.
In the fault diagnosis of rolling bearing, the minor failure information of early stage is usually flooded by noise, be difficult to find and
Extract it is therefore desirable to find an effective signal processing method to improve the signal to noise ratio of bearing vibration signal, with
Just project its fault signature, and traditional signal processing technology effect when processing this nonlinear signal of vibration signal is bad
Or limitation is larger, which limit development further and the research of rolling bearing fault diagnosis.
A kind of flow chart of the method for diagnosing faults of rolling bearing that Fig. 1 provides for the present invention.As shown in figure 1, the axis of rolling
The method for diagnosing faults holding includes：
S10：Obtain the vibration signal of rolling bearing.
In a specific embodiment, vibration signal can be vibration velocity signal, vibration acceleration signal etc..But in reality
In the application of border, vibration acceleration signal more can truly reflect the fault signature of rolling bearing, is preferably carried out accordingly, as one kind
Mode, vibration signal is vibration acceleration signal.Vibration acceleration signal is obtained by vibration acceleration sensor, grasps concrete
In work, vibrating sensor absorption is obtained on rolling bearing pedestal by vibration acceleration signal by magnetic support.
S11：Using EMD empirical mode decomposition algorithm, vibration signal is carried out signal decomposition and obtain decomposed signal.
Using EMD catabolic process it is：
1) all of maximum and the minimum point of signal x (t) to any given signal x (t), are found out first, then
Connect all of maximum with cubic spline curve and form coenvelope line, use same method to construct lower envelope all minimums
Line.Seek meansigma methodss m of upper and lower envelope1, signal x (t) and m1Difference be designated as h1, then obtain：
h1=x (t)-m1(formula 1)
By h1It is considered as x (t), repeat the above steps, until hiWhen meeting two conditions, then it becomes and peels off from primary signal
The first order component out, is designated as c1.
Above-mentioned two condition is：One to be its extreme point number identical with zero passage points or most difference one, and two is thereon
Lower envelope is with regard to time shafts Local Symmetric.
2) by c1It is stripped out from x (t), the surplus next one removes the r of dither mode1, that is, obtain：
r1=x (t)-c1(formula 2)
R1As new x (t), repeat step 1), until the residue signal of N rank is monotonic function it is impossible to decompose again
Go out component.
rN=rN-1-cN(formula 3)
From above-mentioned decomposition method, x (t) is broken down into N number of separable component and a discrepance, that is, obtain：
In above formula：rNT () is discrepance, the average tendency of representation signal；Each component ciT () representation signal is from high frequency to low
The composition of frequency, the frequency content that each frequency band is comprised is different, in same component, not instantaneous frequency in the same time
It is different, local time's distribution of this different frequency composition is to change with signal change itself.
S12：Being converted to frequently of peak extraction, envelope detection and time-frequency domain is carried out using PeakVue to decomposed signal
Spectrum signal.
After EMD decomposition is carried out to vibration signal, peak extraction, envelope detection are carried out to decomposed signal using PeakVue
And time-frequency domain be converted to spectrum signal.
S13：Calculate the failure-frequency of rolling bearing.
In being embodied as, failure-frequency can include outer ring failure-frequency, inner ring failure-frequency, rolling element failure-frequency
Or retainer failure-frequency etc..Calculate each failure-frequency to first have to inquire about rolling bearing information, this information includes ball
Number n, rolling element diameter d, bearing diameter D, roller contact angle α etc., and obtain bearing rotating speed r, unit：Rev/min.
Rolling bearing fault frequency is calculated according to below equation：
S14：Spectrum signal and failure-frequency are carried out contrasting the fault diagnosis result obtaining rolling bearing.
It is understood that the frequency that above-mentioned formula 5-8 calculates is frequency during corresponding hardware failure, if
Corresponding frequency is had then to illustrate that corresponding hardware just breaks down in spectrum signal.In addition, finding in process of the test, fault
The amplitude of the integral multiple of frequency is also larger, therefore, in comparison process, can also add the analysis of the integral multiple of failure-frequency,
The present embodiment is not construed as limiting.Further, since the inner ring of rolling bearing, outer ring, rolling element and retainer are all metals, easily produce
Collision, therefore, vibration signal is easily interfered.In order to optimize this programme, in the comparison process of step S14, can be to event
Barrier one interval of frequency configuration, in this interval if amplitude larger it is also possible to be judged as fault, the present embodiment is no longer superfluous
State.
The method for diagnosing faults of the rolling bearing that the present embodiment provides, including the vibration signal obtaining rolling bearing；Using
Vibration signal is carried out signal decomposition and obtains decomposed signal by EMD empirical mode decomposition algorithm；Using PeakVue, decomposed signal is entered
Row peak extraction, envelope detection and time-frequency domain be converted to spectrum signal；Calculate the failure-frequency of rolling bearing；By frequency spectrum
Signal and failure-frequency carry out contrasting the fault diagnosis result obtaining rolling bearing.As can be seen here, by using EMD empirical modal
Decomposition algorithm is combined with PeakVue method and can realize the fault diagnosis to rolling bearing it is even more important that the method is permissible
Complete online it is ensured that equipment being capable of safe and efficient longtime running.
On the basis of above-described embodiment, also include：Vibration signal is filtered；
Wherein, by high pass filter, vibration signal is filtered.
The impact to vibration signal itself for the interference signal can be reduced by filtering.In order to realize preferable filter effect,
Using high pass filter, vibration signal is filtered in method embodiment.Preferably, the cut-off frequency of high pass filter
For the peak frequency of vibration signal 3-4 times.In being embodied as, can using inner ring failure-frequency as vibration signal maximum
Frequency, that is, the cut-off frequency of high pass filter be 3-4 times of inner ring failure-frequency.
On the basis of above-described embodiment, peak extraction, envelope detection and time domain are carried out to decomposed signal using PeakVue
Specifically include with the spectrum signal that is converted to of frequency domain：
Peak extraction is carried out to decomposed signal with predetermined sampling time interval and obtains peak extraction signal.
Preferably embodiment, predetermined sampling time interval is falling of 2.56 times of the peak frequency of vibration signal
Number, i.e. 1/ (peak frequency of 2.56* vibration signal).
Carry out process using Hilbert transform pairs peak extraction signal and obtain envelope signal.
Using FFT, line translation is entered to envelope signal and obtain spectrum signal.
Because Hilbert transform and FFT are known for those skilled in the art, repeat no more here.
On the basis of above-described embodiment, spectrum signal and failure-frequency are carried out contrasting obtain the fault of rolling bearing and examine
Disconnected result is specially：
Obtain spectrogram using spectrum signal；
Judge whether the frequency range in spectrogram comprises outer ring failure-frequency, inner ring failure-frequency, rolling element fault frequency
Rate, retainer failure-frequency and respective integer multiple frequency；
If it is, output fault cues information；
If it is not, then output regular prompt information；
Wherein, fault diagnosis result includes fault cues information and regular prompt information.
In order to allow those skilled in the art more understand the technical scheme that the present invention provides, a specific example given below
It is illustrated.
In the present invention using rotating speed be 1722r/min, sample rate be 12kps rolling bearing inner ring fault data, through meter
Calculation inner ring failure-frequency is 155.42Hz, and the cut-off frequency of setting high pass filter is 700Hz.Fig. 2 carries for the embodiment of the present invention
For vibration signal filtering before and filtered time domain beamformer.
According to formula (1)～(4), EMD decomposition is carried out to filtered signal, first after decomposition is classified and second component
Time domain waveform as shown in Figure 3.Fig. 3 is EMD decomposition provided in an embodiment of the present invention, the first component after decomposition and second component
Time domain beamformer.
According to above-mentioned peak extraction principle, set sampling time interval and take 1/ (2.56*155.42) ≈ 3ms, then to EMD
The first component decomposing carries out peak extraction.Time domain waveform before and after peak extraction is as shown in Figure 4.Fig. 4 is the embodiment of the present invention
Time domain beamformer before the first component peak extraction providing and after peak extraction.Choose the first component and first carry out Hilbert change
Change its envelope signal of extraction, and using FFT, envelope signal is transformed from the time domain to frequency domain, draw spectrogram, as Fig. 5 institute
Show, Fig. 5 is the PeakVue spectrogram of the first component provided in an embodiment of the present invention.Inner ring failure-frequency as can be seen from Figure 5
It is obvious that respectively 155.3Hz, 310.5Hz, 465.8Hz, 1 frequency multiplication simultaneously turning frequency 29.5 is larger to having for 1 to 3 frequency multiplication
Amplitude, shows have inner ring fault to occur.
Above the method for diagnosing faults of rolling bearing provided by the present invention is described in detail.In description each
Embodiment is described by the way of going forward one by one, and what each embodiment stressed is the difference with other embodiment, each
Between embodiment identical similar portion mutually referring to.For device disclosed in embodiment, because it is public with embodiment
The method opened is corresponding, so description is fairly simple, referring to method part illustration in place of correlation.It should be pointed out that for
For those skilled in the art, under the premise without departing from the principles of the invention, if the present invention can also be carried out
Dry improvement and modification, these improve and modify and also fall in the protection domain of the claims in the present invention.
Professional further appreciates that, in conjunction with the unit of each example of the embodiments described herein description
And algorithm steps, can with electronic hardware, computer software or the two be implemented in combination in, in order to clearly demonstrate hardware and
The interchangeability of software, generally describes composition and the step of each example in the above description according to function.These
Function to be executed with hardware or software mode actually, the application-specific depending on technical scheme and design constraint.Specialty
Technical staff can use different methods to each specific application realize described function, but this realization should
Think beyond the scope of this invention.
The step of the method in conjunction with the embodiments described herein description or algorithm can directly be held with hardware, processor
The software module of row, or the combination of the two is implementing.Software module can be placed in random access memory (RAM), internal memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, depositor, hard disk, moveable magnetic disc, CD-ROM or technology
In known any other form of storage medium in field.
Claims (8)
1. a kind of method for diagnosing faults of rolling bearing is it is characterised in that include：
Obtain the vibration signal of rolling bearing；
Using EMD empirical mode decomposition algorithm, described vibration signal is carried out signal decomposition and obtain decomposed signal；
Believed using the frequency spectrum that is converted to that PeakVue carries out peak extraction, envelope detection and time-frequency domain to described decomposed signal
Number；
Calculate the failure-frequency of described rolling bearing；
Described spectrum signal and described failure-frequency are carried out contrasting the fault diagnosis result obtaining described rolling bearing.
2. the method for diagnosing faults of rolling bearing according to claim 1 is it is characterised in that in described utilization EMD experience
Described vibration signal is carried out also including before signal decomposition obtains decomposed signal by mode decomposition algorithm：Described vibration signal is entered
Row filtering；
Wherein, by high pass filter, described vibration signal is filtered.
3. rolling bearing according to claim 2 method for diagnosing faults it is characterised in that described high pass filter cut
Only frequency is 3-4 times of the peak frequency of described vibration signal.
4. the method for diagnosing faults of rolling bearing according to claim 1 is it is characterised in that described vibration signal is vibration
Acceleration signal；
Wherein, described vibration acceleration signal is obtained by vibration acceleration sensor.
5. the method for diagnosing faults of rolling bearing according to claim 1 is it is characterised in that described employing PeakVue pair
Described decomposed signal carries out peak extraction, envelope detection and time domain and the spectrum signal that is converted to of frequency domain specifically includes：
Peak extraction is carried out to described decomposed signal with predetermined sampling time interval and obtains peak extraction signal；
Carry out process using peak extraction signal described in Hilbert transform pairs and obtain envelope signal；
Using FFT, line translation is entered to described envelope signal and obtain described spectrum signal.
6. the method for diagnosing faults of rolling bearing according to claim 5 is it is characterised in that between the described predetermined sampling time
It is divided into 2.56 times of peak frequency of described vibration signal of inverse.
7. the method for diagnosing faults of rolling bearing according to claim 6 is it is characterised in that described failure-frequency specifically wraps
Include outer ring failure-frequency, inner ring failure-frequency, rolling element failure-frequency or retainer failure-frequency.
8. rolling bearing according to claim 7 method for diagnosing faults it is characterised in that described by described spectrum signal
Carry out contrasting with described failure-frequency and obtain the fault diagnosis result of described rolling bearing and be specially：
Obtain spectrogram using described spectrum signal；
Judge whether the frequency range in described spectrogram comprises described outer ring failure-frequency, inner ring failure-frequency, rolling element event
Barrier frequency, retainer failure-frequency and respective integer multiple frequency；
If it is, output fault cues information；
If it is not, then output regular prompt information；
Wherein, described fault diagnosis result includes described fault cues information and described regular prompt information.
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| CN108426715A (en) * | 2018-06-13 | 2018-08-21 | 福州大学 | Rolling bearing Weak fault diagnostic method based on PSO-VMD-MCKD |
| CN108898050A (en) * | 2018-05-17 | 2018-11-27 | 广东工业大学 | A kind of flexible material process equipment roll shaft performance index calculation method |
| CN110160767A (en) * | 2019-06-14 | 2019-08-23 | 安徽智寰科技有限公司 | Impulse period automatic identification and extracting method and system based on Envelope Analysis |
| CN110163190A (en) * | 2019-06-17 | 2019-08-23 | 郑州大学 | A kind of Fault Diagnosis of Roller Bearings and device |
| CN110657989A (en) * | 2019-09-23 | 2020-01-07 | 红云红河烟草(集团)有限责任公司 | Method and system for monitoring vibration state of tobacco packaging unit |
| WO2020037995A1 (en) * | 2018-08-21 | 2020-02-27 | 北京工业大学 | Two-dimensional quantitative diagnosis method for outer ring defect of rolling bearing |
| CN111060301A (en) * | 2019-12-27 | 2020-04-24 | 中国联合网络通信集团有限公司 | Fault diagnosis method and device |
| CN111178327A (en) * | 2020-01-16 | 2020-05-19 | 佛山科学技术学院 | Deep learning-based bearing state identification method and system |
| CN112162574A (en) * | 2020-10-22 | 2021-01-01 | 中车株洲电机有限公司 | Magnetic suspension bearing rotor vibration control method, device, equipment and storage medium |
| CN113383215A (en) * | 2018-04-30 | 2021-09-10 | 通用电气公司 | System and process for mode-matched bearing vibration diagnostics |
| CN114137063A (en) * | 2021-11-29 | 2022-03-04 | 中国航发哈尔滨轴承有限公司 | Rolling bearing fault diagnosis method based on weak magnetic detection |
| CN114235405A (en) * | 2021-11-24 | 2022-03-25 | 阿里巴巴（中国）有限公司 | Feature extraction method and device of vibration signal, and equipment analysis method and device |
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| CN113383215A (en) * | 2018-04-30 | 2021-09-10 | 通用电气公司 | System and process for mode-matched bearing vibration diagnostics |
| CN108898050A (en) * | 2018-05-17 | 2018-11-27 | 广东工业大学 | A kind of flexible material process equipment roll shaft performance index calculation method |
| CN108426715A (en) * | 2018-06-13 | 2018-08-21 | 福州大学 | Rolling bearing Weak fault diagnostic method based on PSO-VMD-MCKD |
| WO2020037995A1 (en) * | 2018-08-21 | 2020-02-27 | 北京工业大学 | Two-dimensional quantitative diagnosis method for outer ring defect of rolling bearing |
| CN110160767B (en) * | 2019-06-14 | 2021-03-02 | 安徽智寰科技有限公司 | Impact period automatic identification and extraction method and system based on envelope analysis |
| CN110160767A (en) * | 2019-06-14 | 2019-08-23 | 安徽智寰科技有限公司 | Impulse period automatic identification and extracting method and system based on Envelope Analysis |
| CN110163190A (en) * | 2019-06-17 | 2019-08-23 | 郑州大学 | A kind of Fault Diagnosis of Roller Bearings and device |
| CN110163190B (en) * | 2019-06-17 | 2022-05-06 | 郑州大学 | Rolling bearing fault diagnosis method and device |
| CN110657989A (en) * | 2019-09-23 | 2020-01-07 | 红云红河烟草(集团)有限责任公司 | Method and system for monitoring vibration state of tobacco packaging unit |
| CN111060301A (en) * | 2019-12-27 | 2020-04-24 | 中国联合网络通信集团有限公司 | Fault diagnosis method and device |
| CN111178327A (en) * | 2020-01-16 | 2020-05-19 | 佛山科学技术学院 | Deep learning-based bearing state identification method and system |
| CN112162574A (en) * | 2020-10-22 | 2021-01-01 | 中车株洲电机有限公司 | Magnetic suspension bearing rotor vibration control method, device, equipment and storage medium |
| CN112162574B (en) * | 2020-10-22 | 2021-10-22 | 中车株洲电机有限公司 | Magnetic suspension bearing rotor vibration control method, device, equipment and storage medium |
| CN114235405A (en) * | 2021-11-24 | 2022-03-25 | 阿里巴巴（中国）有限公司 | Feature extraction method and device of vibration signal, and equipment analysis method and device |
| CN114137063A (en) * | 2021-11-29 | 2022-03-04 | 中国航发哈尔滨轴承有限公司 | Rolling bearing fault diagnosis method based on weak magnetic detection |
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