CN108875279A - Bearing sound emission signal characteristic extracting method based on EMD and shape filtering - Google Patents
Bearing sound emission signal characteristic extracting method based on EMD and shape filtering Download PDFInfo
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- G06F30/17—Mechanical parametric or variational design
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- 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 the bearing sound emission signal characteristic extracting methods based on EMD and shape filtering, and steps are as follows：Empirical mode decomposition is used first（Empirical Mode decomposition, EMD）Acoustic Emission Signals of Rolling Bearing Fault is decomposed into limited intrinsic mode function by method（Intrinsic Mode Function, IMF）Linear combination；Then the IMF component for being able to reflect fault signature is chosen using correlation coefficient process, then is denoised with IMF component of the shape filtering method to selection.Finally, carrying out Hilbert to the acoustic emission signal after denoising（Hillbert）Envelope spectrum analysis.The present invention can extract Acoustic Emission Signals of Rolling Bearing Fault feature well, be diagnosed to be the position of bearing fault.
Description
Technical field
The bearing sound emission signal characteristic extracting method based on EMD and shape filtering that the present invention relates to a kind of, belongs to lossless
Signal analysis field is detected, is applied to bearing fault and monitors on-line and diagnose.
Background technique
The failure of rotating machinery has 30% to be caused by bearing fault, and rolling bearing is the spare part in common use of rotating machinery
One of.In the process of running, failure can cause the elastic impact of contact surface and generate acoustic emission signal rolling bearing, the signal
Contained it is abundant touch friction information, therefore can monitor and diagnose the failure of rolling bearing using sound emission.
Acoustic emission signal contains largely information relevant to defect, but also contains various interference and noise simultaneously, wherein
Include a large amount of mechanical noise and electromagnetic noise.It is always difficult point that flaw indication can be extracted from ambient noise.Common
Signal processing method is generally Fourier transformation and wavelet analysis, but they have certain limitation.Fourier transformation can only
The frequency domain characteristic of signal is portrayed, the time-domain information of signal can not be provided, and non-stationary signal can not be handled.The selection of wavelet basis
It is the difficult point of wavelet analysis, and it lacks adaptivity, and the leakage for having limit for length to will cause signal energy of wavelet basis.And
Empirical mode decomposition is a kind of adaptive Time-Frequency Analysis Method, is believed according to the time scale feature of itself nonlinear and nonstationary
It number is decomposed, avoids the On The Choice of basic function.
Shape filtering is a kind of Nonlinear harmonic oscillator method.It is based on mathematical morphology, by moving centainly several
The structural element of what shape carries out morphological transformation to signal, achievees the purpose that extract signal and inhibits noise.It is able to maintain letter
Number geometrical characteristic, prevent signal skew.Opening operation filters out burr and isolated point, inhibits positive pulse noise；Closed operation is filled and led up small
Ditch inhibits negative pulse noise.
Summary of the invention
The purpose of the present invention is to solve the problems of the feature extraction hardly possible of current rolling bearing acoustic emission signal, propose
A kind of bearing sound emission signal characteristic extracting method based on empirical mode decomposition and shape filtering.
The purpose of the present invention is what is be achieved through the following technical solutions：Bearing sound emission letter based on EMD and shape filtering
Number feature extracting method, this method comprises the following steps：
（1）Empirical mode decomposition is carried out to the acoustic emission signal of rolling bearing fault, is obtainednA IMF component and a remnants divide
Amount；
（2）The related coefficient of each IMF component and original signal is calculated, the biggish component of related coefficient is chosen, rejects remaining point
Amount；
（3）The denoising of shape filtering method is carried out to each IMF component of selection；
（4）Hilbert transform is carried out to the IMF component after denoising, extracts envelope signal, Fourier's change is carried out to envelope signal
Spectrogram can be obtained by changing；
（5）It is just close with theoretical bearing fault frequency if obviously occurring spectral peak at a certain frequency, then illustrate that event occurs in bearing
Barrier.
As further technical solution, IMF should meet complete one in the empirical mode decomposition method
Data segment in, the number of extreme point and zero crossing must identical or at most difference 1 and no matter which moment signal is in, by it
The average value for the lower envelope that the coenvelope and local minimum that local maximum is constituted are constituted is 0.
As further technical solution, step（1）Empirical mode decomposition method steps are as follows：
The first step determines acoustic emission signalIn all local maximums and minimum point, utilize cubic spline curve point
All Local modulus maximas are not connected and local minizing point forms coenvelope and lower envelope；
Second step, the mean value for calculating upper and lower envelope, are denoted as,From original signalIn separate, obtain：
IfMeetIMFTwo conditions, thenAsAn IMF；IfIt is notAn IMF,Then will
Above step is repeated as original signal, until obtaining meeting IMF condition, separately,As signal's
First IMF,
Third step,From original signalIn separate, obtain：
IfIt is not monotonic function as original signal and repeats above step, untilUntil for a monotonic function,
4th step passes through above step, acoustic emission signalIt is broken down into n IMF component and a monotonic function's
The form of sum：
。
As further technical solution, step（3）The shape filtering method uses platypelloid type structural element, and step is such as
Under：
The first step carries out form open-close to each IMF component（OC）Operation and form are closed-are opened（CO）Operation；
The result of two-way operation is carried out arithmetic average by second step；
As further technical solution, step（4）The step of described Hilbert transform, including following processing：
One real signalHilbert transform be defined as：
Then it obtainsAnalytic signal：
Analytic signalAmplitudeIt is the envelope of original signal：
As further technical solution, step（5）The bearing fault characteristics frequency：
Inner ring failure-frequency：
Outer ring failure-frequency：
Rolling element failure-frequency：
Retainer failure-frequency：
WhereinIt is rotation frequency of spindle,For rolling element diameter,For bearing pitch diameter,For contact angle,For rolling element number.
The beneficial effects of the invention are as follows：Method is simple, it is easy to accomplish, empirical mode decomposition and Morphological Filtering Algorithm is organic
In conjunction with relative to traditional signal processing algorithm, the method for the present invention can be good at inhibiting noise, the information after improving denoising
Quality highlights characteristic frequency, can effectively diagnose the failure of bearing, improves the detection efficiency of defect, reduce maintenance at
This, has actual engineering application value.
Detailed description of the invention
Fig. 1 is that the flow chart of the method for the present invention is also summary diagram of the invention.
Specific embodiment
Invention is further described in detail in the following with reference to the drawings and specific embodiments.
As shown in Figure 1, a kind of bearing sound emission signal characteristic extraction side based on EMD and shape filtering provided by the invention
Method, this method comprises the following steps：
The first step carries out empirical mode decomposition to the acoustic emission signal of rolling bearing fault, obtainsnA IMF component and one it is residual
Remaining component, the operation include the following steps：
Second step determines acoustic emission signalIn all local maximums and minimum point, utilize cubic spline curve point
All Local modulus maximas are not connected and local minizing point forms coenvelope and lower envelope；
Third step, the mean value for calculating upper and lower envelope, are denoted as,From original signalIn separate, obtain：
IfMeetIMFTwo conditions, thenAsAn IMF；IfIt is notAn IMF,Then will
Above step is repeated as original signal, until obtaining meeting IMF condition, separately,As signal's
First IMF；
4th step,From original signalIn separate, obtain：
IfIt is not monotonic function as original signal and repeats above step, untilUntil a monotonic function；
Pass through above step, acoustic emission signalIt is broken down into n IMF component and a monotonic functionSum shape
Formula：
The related coefficient of each IMF component and original signal is calculated, the biggish component of related coefficient is chosen, rejects remaining component；
The denoising of shape filtering method is carried out to each IMF component of selection, which includes the following steps：
First, form open-close all the way is carried out simultaneously to each IMF component（OC）OperationForm is closed-is opened all the way
（CO）Operation；
Secondly, the result of two-way operation is carried out arithmetic average is
；
Finally, wherein g is platypelloid type structural element, and length L takes 0.6 times of inaction interval length；
Further, Hilbert transform is carried out to the IMF component after denoising, extracts envelope signal, envelope signal is carried out in Fu
Leaf transformation can obtain spectrogram, which specifically includes following processing：
One real signalHilbert transform be defined as：
Then it obtainsAnalytic signal：
Analytic signalAmplitudeIt is the envelope of original signal：
It is just close with theoretical bearing fault frequency if further, obviously there is spectral peak at a certain frequency, then illustrate that bearing occurs
Failure, the operation specifically include following processing：
Inner ring failure-frequency：
Outer ring failure-frequency：
Rolling element failure-frequency：
Retainer failure-frequency：
WhereinIt is rotation frequency of spindle,For rolling element diameter,For bearing pitch diameter,For contact angle,For rolling element number.
Claims (6)
1. the bearing sound emission signal characteristic extracting method based on EMD and shape filtering, it is characterized in that successively using following steps：
（1）Empirical mode decomposition is carried out to the acoustic emission signal of rolling bearing fault, is obtainednA IMF component and a remnants divide
Amount；
（2）The related coefficient of each IMF component and original signal is calculated, the biggish component of related coefficient is chosen, rejects remaining point
Amount；
（3）The denoising of shape filtering method is carried out to each IMF component of selection；
（4）Hilbert transform is carried out to the IMF component after denoising, extracts envelope signal, Fourier's change is carried out to envelope signal
Spectrogram can be obtained by changing；
（5）It is just close with theoretical bearing fault frequency if obviously occurring spectral peak at a certain frequency, then illustrate that event occurs in bearing
Barrier.
2. the bearing sound emission signal characteristic extracting method according to claim 1 based on EMD and shape filtering, feature
It is：
The IMF should meet in one section of complete data segment, and the number of extreme point and zero crossing must be identical or at most
It differs 1 and no matter which moment signal is in, the lower packet that the coenvelope and local minimum being made of its local maximum are constituted
The average value of network is 0.
3. the bearing sound emission signal characteristic extracting method according to claim 1 based on EMD and shape filtering, feature
It is：The empirical mode decomposition method step：
The first step determines acoustic emission signalIn all local maximums and minimum point, distinguished using cubic spline curve
It connects all Local modulus maximas and local minizing point forms coenvelope and lower envelope；
Second step, the mean value for calculating upper and lower envelope, are denoted as,From original signalIn separate, obtain：
IfMeetIMFTwo conditions, thenAsAn IMF；IfIt is notAn IMF,Then willMake
Above step is repeated for original signal, until obtaining meeting IMF condition, separately,As signal?
One IMF；
Third step,From original signalIn separate, obtain：
IfIt is not monotonic function as original signal and repeats above step, untilUntil a monotonic function；
4th step passes through above step, acoustic emission signalIt is broken down into n IMF component and a monotonic function's
The form of sum：
。
4. the bearing sound emission signal characteristic extracting method according to claim 1 based on EMD and shape filtering, feature
It is：The shape filtering method uses platypelloid type structural element, and steps are as follows：
The first step carries out form open-close to each IMF component（OC）Operation and form are closed-are opened（CO）Operation；
The result of two-way operation is carried out arithmetic average by second step.
5. the bearing sound emission signal characteristic extracting method according to claim 1 based on EMD and shape filtering, feature
It is：The step of described Hilbert transform, including following processing：
One real signalHilbert transform be defined as：
It obtainsAnalytic signal：
Analytic signalAmplitudeIt is the envelope of original signal：
。
6. the bearing sound emission signal characteristic extracting method according to claim 1 based on EMD and shape filtering, feature
It is：The bearing fault characteristics frequency：
Inner ring failure-frequency：
Outer ring failure-frequency：
Rolling element failure-frequency：
Retainer failure-frequency：
WhereinIt is rotation frequency of spindle,For rolling element diameter,For bearing pitch diameter,For contact angle,For rolling element number.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109632945A (en) * | 2019-01-21 | 2019-04-16 | 中国计量大学 | A kind of noise-reduction method suitable for Pulsed eddy current testing signal |
| CN109682677A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform |
| CN109682678A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of analysis method of fibrous fracture sound |
| CN109682676A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of feature extracting method of the acoustic emission signal of fiber tension failure |
| CN109883704A (en) * | 2019-03-11 | 2019-06-14 | 鲁东大学 | A kind of extracting method of the Rolling Bearing Fault Character based on EEMD and K-GDE |
| CN109948485A (en) * | 2019-03-08 | 2019-06-28 | 浙江工业大学之江学院 | Rotary machine fault characteristic extraction method based on vibration signal correlation analysis |
| CN110096673A (en) * | 2019-04-29 | 2019-08-06 | 河北工业大学 | A kind of EMD improved method suitable for signal decomposition |
| CN110514441A (en) * | 2019-08-28 | 2019-11-29 | 湘潭大学 | A kind of Fault Diagnosis of Roller Bearings based on vibration signal denoising and Envelope Analysis |
| CN110569812A (en) * | 2019-09-12 | 2019-12-13 | 天津工业大学 | envelope demodulation method and envelope demodulation system for fault signal |
| CN110929586A (en) * | 2019-10-29 | 2020-03-27 | 国电大渡河检修安装有限公司 | Fault signal feature extraction method |
| CN111623968A (en) * | 2020-05-08 | 2020-09-04 | 安徽智寰科技有限公司 | Fault feature extraction method based on adaptive morphological filtering |
| CN112098526A (en) * | 2020-07-23 | 2020-12-18 | 西安交通大学 | Near-surface defect feature extraction method for additive product based on laser ultrasonic technology |
| CN112232212A (en) * | 2020-10-16 | 2021-01-15 | 广东石油化工学院 | Triple concurrent fault analysis method and system, large unit device and storage medium |
| CN112747925A (en) * | 2020-12-28 | 2021-05-04 | 西南交通大学 | Rolling bearing fault diagnosis method based on composite morphological filtering |
| CN113029566A (en) * | 2021-02-02 | 2021-06-25 | 王晓东 | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED |
| CN114674561A (en) * | 2022-03-24 | 2022-06-28 | 湖南工业大学 | Bearing fault enhancement diagnosis method based on signal compression and narrow-band filtering |
| CN117454155A (en) * | 2023-12-26 | 2024-01-26 | 电子科技大学 | IGBT acoustic emission signal extraction method based on SSAF and EMD |
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Cited By (21)
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|---|---|---|---|---|
| CN109682677A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of fibrous fracture acoustic emission analysis method based on Hilbert-Huang transform |
| CN109682678A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of analysis method of fibrous fracture sound |
| CN109682676A (en) * | 2018-12-29 | 2019-04-26 | 上海工程技术大学 | A kind of feature extracting method of the acoustic emission signal of fiber tension failure |
| CN109632945A (en) * | 2019-01-21 | 2019-04-16 | 中国计量大学 | A kind of noise-reduction method suitable for Pulsed eddy current testing signal |
| CN109948485A (en) * | 2019-03-08 | 2019-06-28 | 浙江工业大学之江学院 | Rotary machine fault characteristic extraction method based on vibration signal correlation analysis |
| CN109948485B (en) * | 2019-03-08 | 2023-05-12 | 浙江工业大学之江学院 | Rotary machine fault feature extraction method based on vibration signal correlation analysis |
| CN109883704A (en) * | 2019-03-11 | 2019-06-14 | 鲁东大学 | A kind of extracting method of the Rolling Bearing Fault Character based on EEMD and K-GDE |
| CN110096673A (en) * | 2019-04-29 | 2019-08-06 | 河北工业大学 | A kind of EMD improved method suitable for signal decomposition |
| CN110096673B (en) * | 2019-04-29 | 2023-03-14 | 河北工业大学 | EMD improvement method suitable for signal decomposition |
| CN110514441A (en) * | 2019-08-28 | 2019-11-29 | 湘潭大学 | A kind of Fault Diagnosis of Roller Bearings based on vibration signal denoising and Envelope Analysis |
| CN110569812A (en) * | 2019-09-12 | 2019-12-13 | 天津工业大学 | envelope demodulation method and envelope demodulation system for fault signal |
| CN110569812B (en) * | 2019-09-12 | 2022-11-01 | 天津工业大学 | Envelope demodulation method and envelope demodulation system for fault signals |
| CN110929586A (en) * | 2019-10-29 | 2020-03-27 | 国电大渡河检修安装有限公司 | Fault signal feature extraction method |
| CN111623968A (en) * | 2020-05-08 | 2020-09-04 | 安徽智寰科技有限公司 | Fault feature extraction method based on adaptive morphological filtering |
| CN112098526A (en) * | 2020-07-23 | 2020-12-18 | 西安交通大学 | Near-surface defect feature extraction method for additive product based on laser ultrasonic technology |
| CN112232212A (en) * | 2020-10-16 | 2021-01-15 | 广东石油化工学院 | Triple concurrent fault analysis method and system, large unit device and storage medium |
| CN112747925A (en) * | 2020-12-28 | 2021-05-04 | 西南交通大学 | Rolling bearing fault diagnosis method based on composite morphological filtering |
| CN113029566A (en) * | 2021-02-02 | 2021-06-25 | 王晓东 | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED |
| CN114674561A (en) * | 2022-03-24 | 2022-06-28 | 湖南工业大学 | Bearing fault enhancement diagnosis method based on signal compression and narrow-band filtering |
| CN117454155A (en) * | 2023-12-26 | 2024-01-26 | 电子科技大学 | IGBT acoustic emission signal extraction method based on SSAF and EMD |
| CN117454155B (en) * | 2023-12-26 | 2024-03-15 | 电子科技大学 | IGBT acoustic emission signal extraction method based on SSAF and EMD |
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