CN113029566A - Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED - Google Patents
Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED Download PDFInfo
- Publication number
- CN113029566A CN113029566A CN202110144425.XA CN202110144425A CN113029566A CN 113029566 A CN113029566 A CN 113029566A CN 202110144425 A CN202110144425 A CN 202110144425A CN 113029566 A CN113029566 A CN 113029566A
- Authority
- CN
- China
- Prior art keywords
- signal
- eemd
- med
- acoustic emission
- fault
- 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.)
- Pending
Links
- 238000005096 rolling process Methods 0.000 title claims abstract description 31
- 238000000605 extraction Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000001914 filtration Methods 0.000 claims abstract description 8
- 230000035945 sensitivity Effects 0.000 claims abstract description 8
- 238000010606 normalization Methods 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims abstract description 5
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 5
- 239000011159 matrix material Substances 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 2
- 238000012546 transfer Methods 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 17
- 238000001514 detection method Methods 0.000 abstract description 15
- 238000012545 processing Methods 0.000 abstract description 4
- 238000003672 processing method Methods 0.000 abstract 3
- 239000003208 petroleum Substances 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000003921 oil Substances 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000010687 lubricating oil Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
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
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
Landscapes
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- General Physics & Mathematics (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention provides a rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED, which comprises the following steps: s1: EEMD denoising based on sensitivity imf and normalization index optimization is carried out, and a denoising signal is obtained; s2: performing MED filtering to obtain a signal with a strong impact component; s3: and carrying out spectrum analysis and obtaining fault characteristics. The method introduces an acoustic emission waveform signal processing method into fault diagnosis and detection of key bearings in petroleum and petrochemical industry. The improved EEMD signal processing method is introduced into the acoustic emission bearing signal, and filtering processing of on-site complex noise is achieved. The MED signal processing method is introduced into signal processing after EEMD filtering and is used for spectrum analysis so as to realize accurate identification and positioning of rotating equipment fault frequency and fault components.
Description
Technical Field
The invention particularly relates to a rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED.
Background
With the continuous development of scientific technology, the application of rotary machines in various industrial fields is becoming more and more extensive. However, due to the long-term operation of the rotating machine, the rotating machine is extremely susceptible to various levels of damage, resulting in various types of failures. When the fault reaches a certain degree, if the fault cannot be found and maintained in time, unnecessary shutdown can be caused, so that not only is the economic benefit of an enterprise influenced, but also safety accidents can be caused. Therefore, it is very urgent to further improve the reliability and accuracy of fault diagnosis in order to determine the type of fault of the rotary machine more effectively.
At present, bearing fault diagnosis and detection technologies mainly focus on temperature detection methods, vibration detection methods and oil detection methods. Because the temperature rise phenomenon is not obvious when the bearing has pitting, peeling, slight abrasion and other early faults by a temperature detection method, and the bearing temperature change has certain hysteresis in the whole fault development process, the temperature detection method is not suitable for online dynamic monitoring; the vibration detection method is limited by frequency response, sensitivity, quantitative damage evaluation, sensor installation direction and anti-interference capability, so that the accuracy and reliability of early bearing fault diagnosis are low, and the method is not suitable for early state diagnosis; the oil liquid detection method has low sensitivity to faults, and lubricating oil of the bearing needs to be sampled in the bearing fault diagnosis process, so that the method is not suitable for online dynamic detection.
The traditional acoustic emission technology adopts a method based on acoustic emission impact data analysis in the aspect of fault diagnosis of the rotary machine, and the method has the main problems that four fault characteristic frequencies of a bearing cannot be calculated and identified like a vibration analysis method, the existence of a fault and a fault component cannot be reliably confirmed, and the fault state cannot be effectively judged. For this reason, the technical methods based on acoustic emission impingement are far less useful in industrial applications than vibration analysis methods.
Empirical Mode Decomposition (EMD) is a non-stationary signal adaptive Decomposition method completely based on data driving, can decompose a signal from high frequency to low frequency into a finite sum of Intrinsic Mode Functions (IMFs) and residuals with physical significance, and is widely applied in the field of mechanical fault detection. But EMD has severe modal aliasing. The Ensemble Empirical Mode Decomposition (EEMD) is to add white Gaussian noise to a signal to be processed so as to provide enough extreme points for smoothing abnormal events, IMF components obtained by multiple EMD decompositions are subjected to ensemble averaging so as to overcome modal aliasing, so that the EEMD is widely applied to the field of mechanical fault diagnosis, but a series of IMFs obtained by EEMD decompositions are uncontrollable, impact fault characteristics are often only contained in one or partial IMFs, and other components can be regarded as noise or interference signals. Therefore, how to select the characteristic component capable of reflecting the impact fault from the plurality of IMFs obtained by the EEMD decomposition becomes an important difficulty in applying the EEMD method to the mechanical fault diagnosis.
Based on the reasons, a selection criterion of EEMD sensitive IMF is given, an improved EEMD denoising method based on sensitive IMF and normalization index optimization is further provided, and then early weak fault detection of the rolling bearing is provided by combining improved EEMD denoising and MED filtering.
Disclosure of Invention
The invention aims to provide a rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED, aiming at the defects of the prior art, and the rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED can well solve the problems.
In order to meet the requirements, the technical scheme adopted by the invention is as follows: the method for extracting the rolling bearing fault acoustic emission features based on the improved EEMD and the MED comprises the following steps:
s1: EEMD denoising based on sensitivity imf and normalization index optimization is carried out, and a denoising signal is obtained;
s2: performing MED filtering to obtain a signal with a strong impact component;
s3: and carrying out spectrum analysis and obtaining fault characteristics.
The rolling bearing fault acoustic emission feature extraction method based on the improved EEMD and the MED has the advantages that:
(1) the invention utilizes the acoustic emission signal technology to monitor and diagnose the fault of the rotating machinery equipment, and has great improvement on the aspects of sensitivity, diagnosis efficiency, diagnosis reliability and the like of early fault diagnosis.
(2) Compared with other nondestructive detection technologies, the acoustic emission method is a dynamic detection method, the frequency range of a sensor is generally over 100KHz, and the acoustic emission method is far larger than audio noise and vibration noise generated by equipment operation. Therefore, the acoustic emission technology is less prone to noise interference, has better noise immunity, and is more suitable for improving the reliability of fault diagnosis.
(3) The detected signal is from the elastic wave generated by the relative motion impact of the defect of the tested object, can provide real-time information of the early fault defect along with the change of load, time, temperature and the like, has high detection sensitivity, and can detect the elastic wave with the amplitude of 10-14 m.
(4) Compared with the traditional acoustic emission technology based on impact, the bearing fault characteristic frequency can be calculated through waveform flow frequency domain analysis, so that a fault part is determined, and the occurrence of false alarm is greatly reduced.
(5) The improved EEMD method and the MED method are introduced into the AE bearing fault diagnosis, the effectiveness and the superiority of the method on the analysis of the early strong-noise weak fault signal of the bearing are verified, and the method is a further supplement and verification on the analysis result of the impact signal.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 schematically shows a flow chart of a rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED according to an embodiment of the present application.
Fig. 2 schematically shows an exploded view of an EEMD in the rolling bearing fault acoustic emission feature extraction method based on the improved EEMD and MED according to an embodiment of the present application.
Fig. 3 schematically shows a schematic view of a diagnosis flow of the improved EEMD in the rolling bearing fault acoustic emission feature extraction method based on the improved EEMD and the MED according to an embodiment of the present application.
Fig. 4 schematically shows an original waveform and a frequency spectrum diagram of an early failure bearing AE in a rolling bearing failure acoustic emission feature extraction method based on the improved EEMD and MED according to an embodiment of the present application.
Fig. 5 schematically shows a schematic diagram of an original waveform and a frequency spectrum after the EEMD is improved in the rolling bearing fault acoustic emission feature extraction method based on the EEMD and the MED according to an embodiment of the present application.
Fig. 6 schematically shows a schematic diagram of a signal waveform and a frequency spectrum after further MED filtering in the rolling bearing fault acoustic emission feature extraction method based on the improved EEMD and MED according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments.
In the following description, references to "one embodiment," "an embodiment," "one example," "an example," etc., indicate that the embodiment or example so described may include a particular feature, structure, characteristic, property, element, or limitation, but every embodiment or example does not necessarily include the particular feature, structure, characteristic, property, element, or limitation. Moreover, repeated use of the phrase "in accordance with an embodiment of the present application" although it may possibly refer to the same embodiment, does not necessarily refer to the same embodiment.
Certain features that are well known to those skilled in the art have been omitted from the following description for the sake of simplicity.
According to an embodiment of the application, a rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED is provided, and comprises the following steps:
s1: EEMD denoising based on sensitivity imf and normalization index optimization is carried out, and a denoising signal is obtained;
s2: performing MED filtering to obtain a signal with a strong impact component;
s3: and carrying out spectrum analysis and obtaining fault characteristics.
According to an embodiment of the application, the rolling bearing fault acoustic emission feature extraction method based on the improved EEMD and the MED is specifically described as follows:
1. there are two basic modes for collecting acoustic emission from acoustic emission waveform signal, one is burst signal mode. This mode has the characteristic of a momentary sudden appearance and a rapid maximum value, followed by a rapid decay fall. The analysis processing of this kind of signals is to extract characteristic parameters of each burst of pulse or so-called impact signal, and to analyze the extracted more than ten parameters to judge the state of the monitored structure or process. Another signal mode of acoustic emission is continuous-type acoustic emission. As the name implies, a waveform is a continuous flowing waveform. Since the acoustic emission signal frequency range is typically in the tens to hundreds of kilohertz range, which is equivalent to the waveform stream flowing at extremely high speeds, data acquisition often requires rates of 1 megahertz or more. Thus, the waveform stream means a large data stream. When the acoustic emission waveform is used for fault diagnosis of rotating equipment, a certain length of waveform needs to be continuously intercepted for analysis and processing. This requires not only a digitized sampling rate of no less than 1 million data samples per second, but also that each segment of the waveform or data stream be of a certain length. The length of the intercepted waveform flow is determined by the lowest use rotating speed of the rotating equipment or the interested rotating speed, when the lowest use rotating speed is n RPM, the intercepted length of each waveform flow section needs to satisfy T ≧ 5T, wherein T is the period of each rotation, and T is 60/n. Since the waveform stream data has a high frequency and a sufficient length, the waveform stream data contains rich information from a low frequency of several hertz to a high frequency of several hundred kilohertz.
2. Bearing fault diagnosis foundation
The bearing generally comprises an inner ring, an outer ring, rolling elements and a retainer. Each part of the bearing has a characteristic frequency, and the characteristic frequency is related to the speed by the component, and the specific formula is as follows:
outer ring:
inner ring:
rolling element:
a retainer:
wherein α represents a contact angle of the rolling element with the inner and outer races; d represents the bearing pitch circle diameter; d represents the diameter of the rolling body; z represents the number of rolling elements; n represents the bearing RPM, i.e., RPM.
The bearing comprises an inner ring, an outer ring, a rolling body and a retainer, the characteristic frequencies corresponding to the components at the same speed per hour are different, and accordingly the bearing fault component can be judged.
3. Improved EEMD decomposition process
EEMD decomposition is to add different white Gaussian noises with zero mean value and limited amplitude to EMD decomposition signals each time by utilizing the characteristic that the white Gaussian noises have uniform frequency distribution, so that the extreme points of the signals in the whole frequency band are uniformly distributed at intervals, and the problem of large fitting errors of upper and lower envelope curves of the extreme points caused by nonuniform extreme value intervals is solved. The IMFs from multiple EMD decompositions are then ensemble averaged to cancel the added noise, resulting in a set of IMFs that eliminate modal aliasing.
The number of IMF components decomposed by EEMD is uncertain, and the decomposition number is changed when the signal is different, thus the self-adaption is determined. The invention selects the characteristics of Signal to Noise Ratio (SNR), standard deviation (SE), correlation coefficient (R) and Kurtosis (K) to measure the denoising effect and carries out normalization setting. A sensitive IMF number constraint equation is constructed:
when the signal and the noise exist in the same IMF component at the same time, the traditional high-pass filter based on EMD or EEMD can completely retain or completely filter the IMF component, and the denoising effect is poor. Aiming at the condition that the signal and the noise coexist in the same IMF component, the EEMD denoising precision is improved by utilizing the normalized evaluation index and circularly decomposing and reconstructing the signal. The method comprises the steps of reconstructing a signal by selecting a sensitive IMF during EEMD decomposition every time, measuring the denoising effect of the reconstructed signal by a normalized evaluation index, and taking a global optimal point of the normalized index as a denoising iteration termination condition to realize self-adaptive denoising of a bearing fault signal, as shown in FIG. 3.
Attack signal y (n):
wherein x (n) is the fault impact signal, h (i) is the transfer function of the system, and s (n) is the noise interference.
Due to the influence of environmental noise and transmission paths, the information entropy of the signal becomes larger in the convolution calculation process from x (n) to y (n). The MED method aims to find an inverse filter w (l) and obtain a deconvolution signal with minimum entropy by performing convolution calculation on the inverse filter w (n) and the vibration signal y (n), wherein the calculation process is as follows:
where l is the deconvolution filter length.
Is provided with
Is an estimate of w (l). Deconvoluting to obtain a sequence
The smaller the entropy value, the stronger the impulse, the closer to the rolling bearing fault impulse signal x (n), while the noise disturbance is suppressed to the maximum extent, at this time,
tends to be optimal. Sequence adopted by Wiggins
The norm of (a) measures the magnitude of its entropy and uses it as an objective function to solve the optimum
To obtain the optimal deconvolution filter w (n), the objective function is required
The maximum, i.e. first derivative is 0.
The derivatives are taken from both sides of equation (7):
the formula (10) is introduced into the formula (8) as follows:
formula (11) may be regarded as b ═ Aw. Wherein the row vector b can be solved by cross-correlation coefficients of the input and output signals, the matrix A is an L multiplied by L Toeplitz matrix, and the matrix A is solved by autocorrelation coefficients of the input signals, thereby calculating an optimal deconvolution filter w (L) with the length L
w＝A-1b (12)
The influence of environmental noise and a transmission path can be further weakened through an MED method, and the fault impact of the bearing is sharpened.
The above-mentioned embodiments only show some embodiments of the present invention, and the description thereof is more specific and detailed, but should not be construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the claims.
Claims (6)
1. A rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED is characterized by comprising the following steps:
s1: EEMD denoising based on sensitivity imf and normalization index optimization is carried out, and a denoising signal is obtained;
s2: performing MED filtering to obtain a signal with a strong impact component;
s3: and carrying out spectrum analysis and obtaining fault characteristics.
2. The rolling bearing fault acoustic emission feature extraction method based on the EEMD and the MED as claimed in claim 1, wherein: and selecting the signal-to-noise ratio, the standard deviation, the correlation coefficient and the kurtosis characteristic to measure the denoising effect, and performing normalization setting.
3. The rolling bearing fault acoustic emission feature extraction method based on the EEMD and the MED as claimed in claim 1, wherein: when the signal and the noise exist in the same IMF component at the same time, the EEMD denoising precision is improved by using a normalized evaluation index and circularly decomposing a reconstructed signal, the signal is reconstructed by selecting a sensitive IMF during EEMD decomposition each time, the denoising effect of the reconstructed signal is measured by using the normalized evaluation index, and then the global optimum point of the normalized index is taken as a denoising iteration termination condition to realize the self-adaptive denoising of the bearing fault signal.
4. The rolling bearing fault acoustic emission feature extraction method based on the EEMD and the MED as claimed in claim 1, wherein: for the convolved form of the AE impulse signal y (n):
wherein x (n) is the fault impact signal, h (i) is the transfer function of the system, and s (n) is the noise interference.
5. The improved EEMD and MED based rolling bearing fault acoustic emission feature extraction method as claimed in claim 4, wherein: due to the influence of environmental noise and transmission paths, the information entropy of the signal becomes large during the convolution calculation from x (n) to y (n), and the MED method aims to find an inverse filter w (l) to obtain a deconvolution signal with minimum entropy by performing convolution calculation on the inverse filter w (n) and the vibration signal y (n), and the calculation process is as follows:
where l is the deconvolution filter length.
6. The improved EEMD and MED based rolling bearing fault acoustic emission feature extraction method according to claim 5, characterized in that:
is provided with
Is an estimated value of w (l), and is deconvoluted to obtain a sequence
The smaller the entropy value, the stronger the impact, the closer to the rolling bearing fault impact signal x (n), and the noise interference is suppressed to the maximum extent
Tending to be optimal, Wiggins adopts norm of sequence x ^ (n) to measure entropy and uses the norm as an objective function to solve optimal
To obtain the optimal deconvolution filter w (n), the objective function is required
Maximum, i.e. first derivative of 0
The derivatives are taken from both sides of equation (7):
the formula (10) is introduced into the formula (8) as follows:
equation (11) can be regarded as b ═ Aw, where the row vector b can be solved by the cross-correlation coefficients of the input and output signals, and the matrix a is an L × L Toeplitz matrix, which is solved by the autocorrelation coefficients of the input signals, whereby an optimal deconvolution filter w (L) of length L can be calculated
w＝A-1b (12)
The influence of environmental noise and a transmission path can be further weakened through an MED method, and the fault impact of the bearing is sharpened.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110144425.XA CN113029566A (en) | 2021-02-02 | 2021-02-02 | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110144425.XA CN113029566A (en) | 2021-02-02 | 2021-02-02 | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113029566A true CN113029566A (en) | 2021-06-25 |
Family
ID=76459776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110144425.XA Pending CN113029566A (en) | 2021-02-02 | 2021-02-02 | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113029566A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113591248A (en) * | 2021-08-09 | 2021-11-02 | 兰州理工大学 | Bearing fault diagnosis method in mine hoist transmission part |
| CN114969995A (en) * | 2021-11-22 | 2022-08-30 | 昆明理工大学 | Rolling bearing early fault intelligent diagnosis method based on improved sparrow search and acoustic emission |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108875279A (en) * | 2018-07-27 | 2018-11-23 | 中国计量大学 | Bearing sound emission signal characteristic extracting method based on EMD and shape filtering |
| US20190212378A1 (en) * | 2016-09-19 | 2019-07-11 | The University Of New Hampshire | Techniques for Empirical Mode Decomposition (EMD)-Based Noise Estimation |
| WO2019179340A1 (en) * | 2018-03-19 | 2019-09-26 | 河北工业大学 | Eemd- and msb-based failure feature extraction method for rolling-element bearing |
| CN110470475A (en) * | 2019-09-04 | 2019-11-19 | 中国人民解放军空军工程大学航空机务士官学校 | A kind of aero-engine intershaft bearing early-stage weak fault diagnostic method |
| CN112098093A (en) * | 2020-09-15 | 2020-12-18 | 丽水市特种设备检测院 | Bearing fault feature identification method and system |
-
2021
- 2021-02-02 CN CN202110144425.XA patent/CN113029566A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190212378A1 (en) * | 2016-09-19 | 2019-07-11 | The University Of New Hampshire | Techniques for Empirical Mode Decomposition (EMD)-Based Noise Estimation |
| WO2019179340A1 (en) * | 2018-03-19 | 2019-09-26 | 河北工业大学 | Eemd- and msb-based failure feature extraction method for rolling-element bearing |
| CN108875279A (en) * | 2018-07-27 | 2018-11-23 | 中国计量大学 | Bearing sound emission signal characteristic extracting method based on EMD and shape filtering |
| CN110470475A (en) * | 2019-09-04 | 2019-11-19 | 中国人民解放军空军工程大学航空机务士官学校 | A kind of aero-engine intershaft bearing early-stage weak fault diagnostic method |
| CN112098093A (en) * | 2020-09-15 | 2020-12-18 | 丽水市特种设备检测院 | Bearing fault feature identification method and system |
Non-Patent Citations (2)
| Title |
|---|
| 蒋超: "基于EEMD与MED的冲击信号自适应故障特征提取方法", 《中国博士学位论文全文数据库 信息科技辑 (月刊)》, no. 2, pages 136 - 49 * |
| 邹朋等: "基于改进EEMD和MED的滚动轴承早期故障诊断", 《测控技术》, vol. 38, no. 3, pages 47 - 51 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113591248A (en) * | 2021-08-09 | 2021-11-02 | 兰州理工大学 | Bearing fault diagnosis method in mine hoist transmission part |
| CN114969995A (en) * | 2021-11-22 | 2022-08-30 | 昆明理工大学 | Rolling bearing early fault intelligent diagnosis method based on improved sparrow search and acoustic emission |
| CN114969995B (en) * | 2021-11-22 | 2024-02-27 | 昆明理工大学 | Rolling bearing early fault intelligent diagnosis method based on improved sparrow search and acoustic emission |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hu et al. | An adaptive spectral kurtosis method and its application to fault detection of rolling element bearings | |
| Dong et al. | Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings | |
| McCormick et al. | Cyclostationarity in rotating machine vibrations | |
| CN111521400B (en) | Bearing early fault diagnosis method based on EDM and spectral kurtosis | |
| Yan et al. | Energy-based feature extraction for defect diagnosis in rotary machines | |
| CN109883703B (en) | Fan bearing health monitoring and diagnosing method based on vibration signal coherent cepstrum analysis | |
| Schmidt et al. | A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions | |
| CN113029566A (en) | Rolling bearing fault acoustic emission feature extraction method based on improved EEMD and MED | |
| JPH09113416A (en) | Method for diagnosing damage of rolling bearing | |
| CN113607415A (en) | Bearing fault diagnosis method based on short-time stochastic resonance under variable rotating speed | |
| Luo et al. | Multiple discolored cyclic harmonic ratio diagram based on meyer wavelet filters for rotating machine fault diagnosis | |
| He et al. | Bearing condition evaluation based on the shock pulse method and principal resonance analysis | |
| Zhang et al. | Generalized transmissibility damage indicator with application to wind turbine component condition monitoring | |
| Yuan et al. | Dual-mode noise-reconstructed EMD for weak feature extraction and fault diagnosis of rotating machinery | |
| Wang et al. | Multi-domain extreme learning machine for bearing failure detection based on variational modal decomposition and approximate cyclic correntropy | |
| Kestel et al. | Informed sparsity-based blind filtering in the presence of second-order cyclostationary noise | |
| CN115014765B (en) | Method for extracting fault characteristics of rolling bearing retainer through acoustic signals | |
| Orkisz et al. | Spectrum shape based roller bearing fault detection and identification | |
| Xu et al. | Weak faults diagnosis for rolling bearings based on variational Hilbert Huang transform | |
| Sui et al. | Research on envelope analysis for bearings fault detection | |
| Daga et al. | Fast computation of the autogram for the detection of transient faults | |
| Thanagasundram et al. | Autoregressive based diagnostics scheme for detection of bearing faults | |
| Unal et al. | Fault diagnosis of rolling bearing based on feature extraction and neural network algorithm | |
| Deng et al. | Fast Cmspogram: An effective new tool for periodic pulse detection | |
| Huang et al. | Bearing Fault Diagnosis with Varying Speed Conditions Based on Multi-Resolution Singular Value Decomposition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210625 |