CN105547627A - Rotating machinery feature extraction method on the basis of WPT-CEEMD - Google Patents
Rotating machinery feature extraction method on the basis of WPT-CEEMD Download PDFInfo
- Publication number
- CN105547627A CN105547627A CN201610060829.XA CN201610060829A CN105547627A CN 105547627 A CN105547627 A CN 105547627A CN 201610060829 A CN201610060829 A CN 201610060829A CN 105547627 A CN105547627 A CN 105547627A
- Authority
- CN
- China
- Prior art keywords
- signal
- wpt
- ceemd
- frequency
- decomposition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/02—Vibration-testing by means of a shake table
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The present invention discloses a rotating machinery feature extraction method on the basis of a WPT-CEEMD. The noise part is eliminated or useful frequency bands are extracted by utilizing the good local analysis capability of a WPT, then CEEMD adaptive decomposition is performed, and finally few frequency aliasing parts after decomposition are reused for correction by employing the WPT, so that the extraction of feature frequency signals is realized. Through combination of a WPT and a CEEMD, the rotating machinery feature extraction method on the basis of a WPT-CEEMD overcomes disadvantages of existing frequency aliasing, having no adaptability and the like in a fault diagnosis method.
Description
Technical field
The invention belongs to mechanical fault diagnosis field, particularly based on the rotating machinery feature extracting method of WPT-CEEMD.
Background technology
Along with modern industry and scientific and technical develop rapidly, industry has become the standard of a measurement national science technical development.Plant equipment is the carrier of industrial development, for industrial development provides gordian technique, plays more and more important effect in national economy.Simultaneously rotating machinery also more and more towards maximizing, the future development of complicated, precise treatment, the function of plant equipment gets more and more, and performance index are more and more higher, and its composition becomes increasingly complex with structure, and the probability that fault will certainly be made to occur like this increases greatly.
Rolling bearing is most widely used in rotating machinery and is also one of the most flimsy mechanical component, many mechanical faults are all relevant with rolling bearing, its work quality has a great impact the duty of machinery, therefore it is particularly important to carry out fault diagnosis to the rolling bearing in work.Because mechanical equipment vibration signal mostly is nonlinear and non local boundary value problem, traditional Fourier analysis has certain limitation unavoidably.Time-Frequency Analysis Method is as wavelet transformation (Wavelettransform, WT), wavelet package transforms (WaveletPackageTransform, WPT), Hilbert-Huang transform (Hilbert-Huangtransform, HHT), Gabor transformation etc., owing to can provide the local message of signal time domain and frequency domain simultaneously, and be widely used in mechanical fault diagnosis.
In actual signal processing procedure, due to the defect of self Mallat algorithm, can serious frequency alias phenomenon be there is in frequency range segmentation portion and not there is adaptivity, be directly used in fault-signal extraction effect not good in traditional WT and WPT.Signal adaptive can be decomposed into limited intrinsic mode function (IntrinsicModeFunction by the EMD in Hilbert-Huang transform, IMF), the each IMF component decomposited comprises the local feature signal of original signal Different time scales, but also there is serious modal overlap shortcoming.For this shortcoming, population mean empirical mode decomposition (EnsembleEmpiricalModeDecomposition is also been proposed in the basis of EMD, and the improving one's methods of EEMD---complementary population mean empirical mode decomposition (ComplementaryEnsembleEmpiricalModeDecomposition, CEEMD) EEMD).Two kinds of methods, after adaptive decomposition, suppress the frequency alias defect of EMD still to there is a small amount of modal overlap phenomenon in low frequency part though effective.
Summary of the invention
In order to solve the technical matters that above-mentioned background technology proposes, the present invention aims to provide the rotating machinery feature extracting method based on WPT-CEEMD, is combined by WPT and CEEMD, effectively inhibits the mode mixing after decomposition.
In order to realize above-mentioned technical purpose, technical scheme of the present invention is:
Based on the rotating machinery feature extracting method of WPT-CEEMD, comprise the following steps:
(1) characteristic of rotating machines vibration signal is gathered;
(2) FFT conversion is carried out to the vibration signal gathered, re-use WPT transfer pair signal and carry out denoising Processing;
(3) CEEMD adaptive decomposition is carried out to the signal after denoising Processing;
(4) extract the frequency signal of the IMF component decomposited, make the spectrogram of each IMF component;
(5) check whether to there is serious modal overlap according to the corresponding relation of frequency and amplitude, namely whether the detail signal of multiple different frequency is broken down in same IMF component, if there is serious modal overlap, then return step (3), otherwise proceed to step (6);
(6) if the signal of same frequency by adaptive decomposition in adjacent IMF component, then to occur the part of modal overlap utilize WPT convert revise, extract characteristic signal.
Further, WPT transfer pair signal is used to carry out the detailed process of denoising Processing in step (2):
A () selection wavelet basis also, after determining the level decomposed, carries out WAVELET PACKET DECOMPOSITION to signal;
B (), for given entropy standard, determines best wavelet packet basis;
C () carries out threshold value quantizing to the coefficient of best wavelet packet basis;
D () is reconstructed the wavelet packet coefficient after threshold value quantizing, obtain reconstruction signal.
Further, the detailed process of step (3):
(I) respectively to add in signal x (t) after denoising Processing one group of equal length, given standard deviation, the positive and negative contrary normal distribution white noise of symbol, thus formed two new signals;
(II) carries out EMD decomposition respectively to adding two signals after white noise, obtains its respective IMF component;
(III), according to the decomposition frequency n of setting, repeats step (I), (II) n time, and requires to add new normal distribution white noise at every turn;
(IV) is decomposed the IMF component obtained and is amounted to 2n group, divided by 2n after being added by this 2n group IMF component, asking it average, obtains 1 group of IMF component.
Further, the amplitude of described normal distribution white noise is that the standard deviation of signal x (t) is multiplied by coefficient f, wherein 0<f<1.
Further, when decomposition frequency n gets 100, the span of coefficient f is 0.01<f<0.5.
Further, in step (5), if there is serious modal overlap, when returning step (3), need to redefine decomposition frequency n.
Adopt the beneficial effect that technique scheme is brought:
The present invention's application CEEMD decomposes signal, effectively inhibit the modal overlap problem that traditional E MD decomposes, and the partial analysis ability using WPT good is revised the part that there is modal overlap after decomposition again, thus reach the accurate extraction to detail signal.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention;
Fig. 2 comprises (a), (b), (c) 3 width figure, is respectively signal y1, y2 and simulate signal s (t) oscillogram;
Fig. 3 comprises (a), (b) 2 width figure, is respectively IMF1, IMF2 oscillogram that s (t) decomposes through EMD;
Fig. 4 comprises (a), (b), (c), (d) 4 width figure, is respectively IMF1, IMF2, IMF3, IMF4 oscillogram that s (t) decomposes through CEEMD;
Fig. 5 is the vibration signal time-domain diagram that actual acquisition obtains;
Fig. 6 is the vibration signals spectrograph figure that actual acquisition obtains;
Fig. 7 is that the vibration signal obtained actual acquisition carries out the pretreated spectrogram of WPT;
Fig. 8 comprises (a), (b), (c) 3 width figure, is respectively the time-domain diagram of IMF6, IMF7, IMF8 that vibration signal that actual acquisition obtains decomposes through CEEMD;
Fig. 9 comprises (a), (b), (c) 3 width figure, is respectively the spectrogram of IMF6, IMF7, IMF8 that vibration signal that actual acquisition obtains decomposes through CEEMD;
The time-domain diagram of the frequency of operation component that Figure 10 comprises (a), (b) 2 width figure, WPT extract after revising and spectrogram.
Embodiment
Below with reference to accompanying drawing, technical scheme of the present invention is described in detail.
Process flow diagram of the present invention as shown in Figure 1, based on the rotating machinery feature extracting method of WPT-CEEMD, comprises the following steps:
(1) characteristic of rotating machines vibration signal is gathered;
(2) carry out FFT conversion to the vibration signal gathered, re-use WPT transfer pair signal and carry out denoising Processing, suppress the nonuseable part in signal, strengthen useful part in signal, its detailed process is as follows:
(a) select a wavelet basis and determine decompose level after, WAVELET PACKET DECOMPOSITION is carried out to signal, wavelet packet ground floor to decompose signal with 1/2 of sample frequency for signal to be divided into height two frequency ranges by mid point, ground floor is decomposed two frequency ranges and obtains four frequency ranges with the sample frequency of 1/4 and 3/4 for mid point carries out decomposition more respectively by second layer decomposition, by that analogy, that can extract according to actual signal needs selection Decomposition order;
B (), for given entropy standard, determines best wavelet packet basis;
C () carries out threshold value quantizing to the coefficient of best wavelet packet basis;
D () is reconstructed the wavelet packet coefficient after threshold value quantizing, obtain reconstruction signal.
(3) carry out CEEMD adaptive decomposition to the signal after denoising Processing, its detailed process is as follows:
(I) respectively to add in signal x (t) after denoising Processing one group of equal length, given standard deviation, the positive and negative contrary normal distribution white noise of symbol, thus formed two new signals;
(II) carries out EMD decomposition respectively to adding two signals after white noise, obtains its respective IMF component;
(III), according to the decomposition frequency n of setting, repeats step (I), (II) n time, and requires to add new normal distribution white noise at every turn;
(IV) is decomposed the IMF component obtained and is amounted to 2n group, divided by 2n after being added by this 2n group IMF component, asking it average, obtains 1 group of IMF component.
The amplitude of normal distribution white noise is that the standard deviation of signal x (t) is multiplied by coefficient f, wherein 0<f<1, when decomposition frequency n gets 100, the span of coefficient f is 0.01<f<0.5.In actual applications, the coefficient adding the amplitude of white noise adjusts according to the size of signal institute Noise after pre-service, and the coefficient of the white noise amplitude of adding when the larger CEEMD of institute's Noise decomposes in signal is larger;
(4) extract the frequency signal of the IMF component decomposited, make the spectrogram of each IMF component;
(5) check whether to there is serious modal overlap according to the corresponding relation of frequency and amplitude, namely whether the detail signal of multiple different frequency is broken down in same IMF component, if there is serious modal overlap, then return step (3), now need to redefine decomposition frequency n, otherwise proceed to step (6);
(6) if the signal of same frequency (especially at low-band signal) by adaptive decomposition in adjacent IMF component, then to occurring that the part of modal overlap utilizes WPT to convert and revises, the signal of the different frequency in same IMF component is separated, be added in the signal time domain of same frequency again, required frequency signal can be extracted.
As shown in Figure 2, first emulating signal s (t) is be formed by stacking i.e. s (t)=y1+y2 by low frequency sinusoidal component y1 (t) and high frequency intermittent signal y2 (t), wherein
y1＝sin(20π*t)0≤t≤1(1)
Simulate signal sample frequency is 1kHz, and the sampling time is 1s.EMD and CEEMD is adopted to decompose simulate signal s (t) respectively, decomposing number of times is 50 times, add 0.1 times that white noise amplitude standard deviation is simulate signal, result respectively as shown in Figure 3, Figure 4, as seen from Figure 3, the signal that two frequency phase-differences are very large is decomposed in same component and IMF1 by EMD, has occurred modal overlap phenomenon.Fig. 4 can find out that CEEMD decomposes the IMF1 obtained and well extracts high frequency intermittent signal and amplitude only has very little change; IMF2, IMF3 contain two a small amount of source signals, have occurred a small amount of modal overlap phenomenon; IMF4 extracts Low Frequency Sine Signals.Correlation analysis is done to IMF1 and y2 (t) and can 97.12% be reached.Thus demonstrate the modal overlap shortcoming that CEEMD effectively inhibits EMD.
Again for actual vibration signal, the dynamic signal analyzer of OROSR3X system is adopted to carry out test experiments to horizontal spiral centrifuge (abbreviation horizontal screw centrifuge) at the vibration signal of working speed operation phase.Sample frequency is 12.8kHz, and the sampling time is 20s, and horizontal screw centrifuge correlation parameter is as shown in table 1.Fig. 5 is the time-domain signal collected.
Table 1
In order to extract horizontal screw centrifuge frequency of operation i.e. 1 order component 55Hz, first select to carry out FFT conversion to the vibration signal collected and make its spectrogram as shown in Figure 6, the spectrogram according to sample frequency and signal carries out WPT pre-service.
As shown in Figure 7, after can finding out pre-service, high frequency noise components is eliminated the pretreated signal spectrum figure of WPT, extracts useful frequency band, and from spectrogram, obtain the corresponding relation of each frequency content and amplitude for next step CEEMD decomposes.
Pretreated signal is carried out CEEMD adaptive decomposition, through test of many times, selects to decompose and repeat number of steps.Getting decomposition frequency n is 50, and the amplitude standard deviation of the white noise of interpolation is pretreated 0.1 times of WPT.Because the IMF component after CEEMD adaptive decomposition is arranged in order from high to low according to frequency band, the time-domain diagram of IMF6, IMF7, IMF8 tri-components after Fig. 8, Fig. 9 decompose respectively and spectrogram.
According to the frequency in the spectrogram of IMF component and amplitude corresponding relation, contrast with pretreated signal spectrum Fig. 7, after CEEMD adaptive decomposition can be found out, still there is a small amount of modal overlap part at the IMF component that low frequency part is adjacent, the signal of 55Hz is decomposed in IMF7, IMF8 two components by adaptive.
WPT process is used again to a small amount of modal overlap part existed, utilize the partial analysis ability of WPT, frequency band in IMF7 is decomposed again, 55Hz is separated with 2 frequency multiplication 110Hz, then the signal of isolated 55Hz to be joined in IMF8 thus the extraction reached 1 order component.The time-domain spectral figure of the 55Hz component extracted as shown in Figure 10.
By finding out with the decomposition result of emulation experiment and actual vibration signal, although CEEMD adaptive decomposition effectively improves EMD containing the modal overlap problem in discontinuous point signal, but still there is a small amount of frequency alias in low frequency part, again revised in conjunction with WPT for this problem, by contrasting with the frequency calculating gained, good achieves bearing failure diagnosis.
Above embodiment is only and technological thought of the present invention is described, can not limit protection scope of the present invention with this, and every technological thought proposed according to the present invention, any change that technical scheme basis is done, all falls within scope.
Claims (6)
1., based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that, comprise the following steps:
(1) characteristic of rotating machines vibration signal is gathered;
(2) FFT conversion is carried out to the vibration signal gathered, re-use WPT transfer pair signal and carry out denoising Processing;
(3) CEEMD adaptive decomposition is carried out to the signal after denoising Processing;
(4) extract the frequency signal of the IMF component decomposited, make the spectrogram of each IMF component;
(5) if the detail signal of multiple different frequency is broken down in same IMF component, then illustrate to there is serious modal overlap, now return step (3), otherwise proceed to step (6);
(6) if the signal of same frequency by adaptive decomposition in adjacent IMF component, then to occur the part of modal overlap utilize WPT convert revise after, extract characteristic signal.
2. according to claim 1 based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that, in step (2), use WPT transfer pair signal to carry out the detailed process of denoising Processing:
A () selection wavelet basis also, after determining the level decomposed, carries out WAVELET PACKET DECOMPOSITION to signal;
B (), for given entropy standard, determines best wavelet packet basis;
C () carries out threshold value quantizing to the coefficient of best wavelet packet basis;
D () is reconstructed the wavelet packet coefficient after threshold value quantizing, obtain reconstruction signal.
3. according to claim 1 based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that, the detailed process of step (3):
(I) is respectively to the signal after denoising Processing
x(
t) in add one group of equal length, given standard deviation, the positive and negative contrary normal distribution white noise of symbol, thus formed two new signals;
(II) carries out EMD decomposition respectively to adding two signals after white noise, obtains its respective IMF component;
(III), according to the decomposition frequency n of setting, repeats step (I), (II) n time, and requires to add new normal distribution white noise at every turn;
(IV) is decomposed the IMF component obtained and is amounted to 2n group, divided by 2n after being added by this 2n group IMF component, asking it average, obtains 1 group of IMF component.
4. according to claim 3 based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that: the amplitude of described normal distribution white noise is signal
x(
t) standard deviation be multiplied by coefficient f, wherein 0<f<1.
5. according to claim 4 based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that: when decomposing frequency n and getting 100, the span of coefficient f is 0.01<f<0.5.
6. according to claim 3 based on the rotating machinery feature extracting method of WPT-CEEMD, it is characterized in that: in step (5), if there is serious modal overlap, when returning step (3), needing to redefine decomposition frequency n.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610060829.XA CN105547627B (en) | 2016-01-28 | 2016-01-28 | Rotating machinery feature extracting method based on WPT-CEEMD |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610060829.XA CN105547627B (en) | 2016-01-28 | 2016-01-28 | Rotating machinery feature extracting method based on WPT-CEEMD |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105547627A true CN105547627A (en) | 2016-05-04 |
| CN105547627B CN105547627B (en) | 2018-06-12 |
Family
ID=55826971
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610060829.XA Active CN105547627B (en) | 2016-01-28 | 2016-01-28 | Rotating machinery feature extracting method based on WPT-CEEMD |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105547627B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105891707A (en) * | 2016-05-05 | 2016-08-24 | 河北工业大学 | Opening-closing fault diagnosis method for air circuit breaker based on vibration signals |
| CN106568607A (en) * | 2016-11-04 | 2017-04-19 | 东南大学 | Rub-impact sound emission fault diagnosis method based on empirical wavelet transformation |
| CN106568589A (en) * | 2016-11-04 | 2017-04-19 | 东南大学 | Rubbing acoustic emission denoise method based on empirical wavelet transform |
| CN108229382A (en) * | 2017-12-29 | 2018-06-29 | 广州供电局有限公司 | Vibration signal characteristics extracting method, device, storage medium and computer equipment |
| CN111337767A (en) * | 2020-02-25 | 2020-06-26 | 河北工业大学 | Resonant wave reducer fault analysis method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394658A (en) * | 2011-10-16 | 2012-03-28 | 西南科技大学 | Composite compression method oriented to mechanical vibration signal |
| CN103091096A (en) * | 2013-01-23 | 2013-05-08 | 北京信息科技大学 | Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform |
| CN104198183A (en) * | 2014-09-17 | 2014-12-10 | 重庆大学 | Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method |
-
2016
- 2016-01-28 CN CN201610060829.XA patent/CN105547627B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102394658A (en) * | 2011-10-16 | 2012-03-28 | 西南科技大学 | Composite compression method oriented to mechanical vibration signal |
| CN103091096A (en) * | 2013-01-23 | 2013-05-08 | 北京信息科技大学 | Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform |
| CN104198183A (en) * | 2014-09-17 | 2014-12-10 | 重庆大学 | Wind turbine drive chain vibration noise suppression and incipient fault feature extraction method |
Non-Patent Citations (7)
| Title |
|---|
| LIYE ZHAO 等: "Rolling bearing Fault Diagnosis Based on CEEMD and Time Series Modeling", 《MATHEMATICAL PROBLEMS IN ENGINEERING》 * |
| 冯宏亮: "基于最优小波包基的信号去噪方法研究", 《西安文理学院学报》 * |
| 喻伟 等: "基于CEEMD 和小波包变换的重力数据信噪分离方法", 《软件》 * |
| 王红军 等: "基于EEMD和小波包变换的早期故障敏感特征获取", 《北京理工大学学报》 * |
| 赵迎 等: "CEEMD与小波变换联合去噪方法研究", 《地球物理学进展》 * |
| 陆森林 等: "CEEMD-FFT在滚动轴承故障诊断中的应用", 《郑州大学学报(工学版)》 * |
| 韩璞 等: "汽轮机振动信号的最优小波包基消噪与检测", 《中国动力工程学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105891707A (en) * | 2016-05-05 | 2016-08-24 | 河北工业大学 | Opening-closing fault diagnosis method for air circuit breaker based on vibration signals |
| CN106568607A (en) * | 2016-11-04 | 2017-04-19 | 东南大学 | Rub-impact sound emission fault diagnosis method based on empirical wavelet transformation |
| CN106568589A (en) * | 2016-11-04 | 2017-04-19 | 东南大学 | Rubbing acoustic emission denoise method based on empirical wavelet transform |
| CN108229382A (en) * | 2017-12-29 | 2018-06-29 | 广州供电局有限公司 | Vibration signal characteristics extracting method, device, storage medium and computer equipment |
| CN111337767A (en) * | 2020-02-25 | 2020-06-26 | 河北工业大学 | Resonant wave reducer fault analysis method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105547627B (en) | 2018-06-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105547627A (en) | Rotating machinery feature extraction method on the basis of WPT-CEEMD | |
| Lei et al. | A review on empirical mode decomposition in fault diagnosis of rotating machinery | |
| Qin et al. | M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems | |
| Qin et al. | Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis | |
| Yang et al. | A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition | |
| Żak et al. | Application of ARMA modelling and alpha-stable distribution for local damage detection in bearings | |
| CN104636609A (en) | Signal combined denoising method based on empirical mode decomposition (EMD) and wavelet analysis | |
| CN103900815A (en) | Rolling bearing fault diagnosis method based on EEMD and distribution fitting testing | |
| Guo et al. | Enhancing the ability of ensemble empirical mode decomposition in machine fault diagnosis | |
| CN108458871A (en) | A kind of gearbox fault recognition methods based on improvement experience wavelet transformation | |
| CN109029999B (en) | Rolling bearing fault diagnosis method based on enhanced modulation bispectrum analysis | |
| Qin et al. | Multicomponent decomposition by wavelet modulus maxima and synchronous detection | |
| CN104635223A (en) | Laser echo denoising method based on empirical mode decomposition and fractional Fourier transformation | |
| Li et al. | Derivative and enhanced discrete analytic wavelet algorithm for rolling bearing fault diagnosis | |
| CN111504640B (en) | Weighted sliding window second-order synchronous compression S transformation bearing fault diagnosis method | |
| Lv et al. | Longitudinal synchroextracting transform: A useful tool for characterizing signals with strong frequency modulation and application to machine fault diagnosis | |
| Sun et al. | Application of wavelet soft threshold denoising algorithm based on EMD decomposition in vibration signals | |
| CN103674550A (en) | Real-time mixing denoising method for rolling bearing static monitoring signal | |
| Fa-jun et al. | Compound fault diagnosis of gearbox based on wavelet packet transform and sparse representation classification | |
| Liu et al. | Synchronous fault feature extraction for rolling bearings in a generalized demodulation framework | |
| JP2015004573A (en) | Frequency analyzer | |
| CN115902396B (en) | Resonance detection method and device for large wind power grid-connected system | |
| CN106840379A (en) | A kind of failure analysis methods of mechanical oscillation signal | |
| CN112345248A (en) | Fault diagnosis method and device for rolling bearing | |
| Huang et al. | Nonconvex regularized sparse representation in a tight frame for gear fault diagnosis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP02 | Change in the address of a patent holder | ||
| CP02 | Change in the address of a patent holder |
Address after: Room 420, block C, Kechuang headquarters building, No. 320, pubin Road, Jiangpu street, Pukou District, Nanjing, Jiangsu Patentee after: NANJING University OF INFORMATION SCIENCE & TECHNOLOGY Address before: 210044 Nanjing City, Pukou Province, Nanjing Road, No. 219, No. six, No. Patentee before: NANJING University OF INFORMATION SCIENCE & TECHNOLOGY |