CN109029996B - Wheel hub bearing fault diagnosis method - Google Patents

Wheel hub bearing fault diagnosis method Download PDF

Info

Publication number
CN109029996B
CN109029996B CN201811057836.XA CN201811057836A CN109029996B CN 109029996 B CN109029996 B CN 109029996B CN 201811057836 A CN201811057836 A CN 201811057836A CN 109029996 B CN109029996 B CN 109029996B
Authority
CN
China
Prior art keywords
signal
fault
wavelet packet
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.)
Active
Application number
CN201811057836.XA
Other languages
Chinese (zh)
Other versions
CN109029996A (en
Inventor
向家伟
王璐
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wenzhou University Cangnan Research Institute
Original Assignee
Wenzhou University Cangnan Research Institute
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Wenzhou University Cangnan Research Institute filed Critical Wenzhou University Cangnan Research Institute
Priority to CN201811057836.XA priority Critical patent/CN109029996B/en
Publication of CN109029996A publication Critical patent/CN109029996A/en
Application granted granted Critical
Publication of CN109029996B publication Critical patent/CN109029996B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention belongs to the field of automobile maintenance, and relates to a hub bearing fault diagnosis method based on wavelet packet enhanced empirical mode decomposition. First, the original signal is decomposed using wavelet packet decomposition and sub-signals are obtained. And filtering the lowest frequency component in the subsignals and reserving the rest high-frequency components. Secondly, the series of sub-signals is added to the original signal so that they are uniformly distributed over the entire time-frequency space of the signal. And thirdly, further decomposing the mixed signal into a plurality of eigenmode functions by utilizing wavelet packet enhanced empirical mode decomposition, and extracting components containing high fault characteristic information for reconstruction. And finally, performing Hilbert envelope analysis on the reconstructed signal and diagnosing the type of the bearing fault. The method of the invention utilizes wavelet packet decomposition to finely decompose and denoise the original signal, thereby effectively improving the signal-to-noise ratio; the wavelet packet enhanced empirical mode decomposition further decomposes the signals into local characteristic signals with different time scales, and the bearing fault type can be visually detected through envelope demodulation.

Description

Wheel hub bearing fault diagnosis method
Technical Field
The invention relates to the field of automobile maintenance, in particular to a hub bearing fault diagnosis method based on wavelet packet enhanced empirical mode decomposition.
Background
The bearing is widely applied to rotary machines as a supporting rotating part. In an automobile, at least 50 sets of bearings are arranged at different rotating parts. The hub bearing serves as a key part in an automobile suspension system, and has the functions of bearing, reducing friction of a rotating pair, transmitting torque, providing accurate guidance for rotation of a hub and the like. The hub axle has a high rotational speed and is simultaneously subjected to a radial gravitational load, an axial load during steering and a torque of the drive shaft. Therefore, the safety and comfort of the automobile during running are directly affected by the quality of the hub bearing. To avoid safety problems caused by hub bearing failure, it is important to detect and diagnose the hub bearing failure.
The hub bearing is interfered by strong noise due to complex working environment, and fault characteristics are easily covered by interference information. When a faulty bearing is running with the car, the resulting vibration signal is a non-stationary signal. Due to load, clearance, friction and noise interference in the environment, the acquired vibration signal contains complex components and the fault characteristics cannot be directly extracted. The traditional fault diagnosis methods such as Fourier transform, spectrum analysis and the like have poor extraction effect on weak faults of the bearing.
Wavelet packet decomposition is an improvement and development of a wavelet analysis method, and can subdivide high-frequency and low-frequency parts of a signal at the same time, so that the decomposition is more precise, and effective components of the signal are better reserved while noise is eliminated. The wavelet packet enhanced empirical mode decomposition is a novel self-adaptive time-frequency analysis method, and a series of wavelet packet decomposed sub-signals are added into an original signal, so that the fault characteristics of the original signal are enhanced, and the mode aliasing phenomenon is effectively inhibited. In the market, wavelet packet decomposition and wavelet packet enhancement empirical modes are not applied to the field of hub bearing fault diagnosis.
Disclosure of Invention
In order to overcome the problem that the faults of the hub bearing are difficult to extract under the background of strong noise, the inventor provides a hub bearing fault diagnosis method based on wavelet packet enhanced empirical mode decomposition, and can quickly and accurately diagnose the weak faults of the hub bearing.
A method of diagnosing a failure of a hub bearing, comprising the steps of:
firstly, acquiring an original fault signal of an automobile hub bearing;
decomposing original fault signals: decomposing and decomposing an original fault signal by utilizing a wavelet packet to obtain a series of sub-signals, removing the lowest frequency component in the sub-signals, and reserving the high frequency component containing fault information;
thirdly, decomposing and reconstructing fault signals by utilizing a wavelet packet enhanced mode: adding the sub-signals in the step II into the original fault signal to carry out empirical mode decomposition, so as to decompose the sub-signals into components of different frequency bands in a self-adaptive manner, and reconstructing the fault signal according to a kurtosis principle;
analysis result: performing Hilbert envelope analysis on the reconstructed fault signal in the step (c) and calculating to obtain an analysis result;
diagnosing the fault type: and (4) calculating a theoretical value of the bearing fault characteristic frequency, and comparing the theoretical value with the analysis result in the step (IV) to determine the fault type.
In order to further improve the scheme, the invention is further provided with: and the wavelet packet decomposition in the second step is to perform high-frequency decomposition and low-frequency decomposition on the original signal at the same time to obtain a high-frequency signal and a low-frequency signal, and then continuously decompose the obtained high-frequency signal and the low-frequency signal.
Wavelet packet functionIs defined as:
Figure GDA0002523869640000021
j is a scale parameter for locating the frequency, k is a translation parameter for locating the time, and m is an oscillation parameter.
Wavelet packet coefficient:
Figure GDA0002523869640000022
representing the mth wavelet packet coefficient at the j-level decomposition scale.
The original signal X (t) is
Figure GDA0002523869640000023
And N is the signal length.
Wavelet packet decomposition altogether produces 2jWavelet packet coefficient Cj,mLength of N/2jThe wavelet packet coefficient equation is
Figure GDA0002523869640000031
The invention is further set that the wavelet packet enhanced modal decomposition comprises the following steps:
the method comprises the following steps: adding wavelet packet decomposed subsignals w (j, m), i.e. x, to the signal x (t)m(t)＝x(t)+w(j,m)。
Step two: for signal xm(t) performing empirical mode decomposition.
Step three: and repeating the process from the step one to the step two m times, and adding one sub-signal every time the process is repeated.
Step four: averaging the eigenmode function components of each order obtained after m-time decomposition to obtain an overall average value Ij(t)，
Figure GDA0002523869640000032
Denotes the jth eigenmode function component, and Q denotes the number of decompositions.
The principle of wavelet packet enhanced modal decomposition is to add a series of sub-signals decomposed by wavelet packets to an original signal, so that the sub-signals are uniformly distributed on the whole time-frequency space of the signal. At this time, signals of different scale features are adaptively spread to an appropriate reference scale. After multiple screening and averaging, the noises are mutually counteracted, and the signal keeps stable, so that the fault characteristics of the original signal are enhanced. Thus, both the original signal is preserved and modal aliasing is attenuated.
The invention is further set that the theoretical value calculating method of the bearing fault characteristic frequency in the fifth step comprises a bearing fault outer ring characteristic frequency calculating method and a bearing fault inner ring characteristic frequency calculating method.
The invention is further set that the theoretical value calculation method of the bearing fault characteristic frequency in the fifth step comprises a bearing inner ring fault calculation formula, and the calculation formula is as follows:
Figure GDA0002523869640000033
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
The invention is further set that the theoretical value calculation method of the bearing fault characteristic frequency in the fifth step comprises a bearing outer ring fault calculation formula, and the calculation formula is as follows:
Figure GDA0002523869640000041
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
The hub bearing has complex working condition and strong background noise of the working environment. Therefore, the fault feature information in the original fault signal is often buried in the noise, and the common filtering process cannot effectively remove the noise and extract the fault feature. The wavelet packet provides a flexible noise elimination means and effectively separates noise. Wavelet packet enhanced empirical mode decomposition further adaptively decomposes the signal into a series of components. And by selecting the optimal reconstruction component, the fault characteristic frequency is effectively extracted. The method can quickly and accurately diagnose weak faults which cannot be diagnosed by the traditional method. The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of the steps of a method of the present invention;
FIG. 2 is a flowchart of wavelet packet enhanced empirical mode decomposition in accordance with the present invention;
FIG. 3 is a time domain diagram of an outer ring original signal;
FIG. 4 is an outer ring original signal envelope spectrogram;
FIG. 5 is a diagram showing the energy distribution of the eigenmode functions IMFs in example 1;
FIG. 6 is a time domain diagram of a wavelet packet enhanced empirical mode decomposition reconstructed signal in example 1;
FIG. 7 is a reconstructed signal envelope spectrum of a wavelet packet enhanced Empirical Mode Decomposition (EMD) in example 1;
FIG. 8 is a time domain diagram of the inner ring of the original signal;
FIG. 9 is an inner ring original signal envelope spectrogram;
FIG. 10 is a diagram showing the energy distribution of the eigenmode functions IMFs in example 2;
FIG. 11 is a time domain diagram of a wavelet packet enhanced empirical mode decomposition reconstructed signal in example 2;
FIG. 12 is a spectrum diagram of a wavelet packet enhanced EMD reconstructed signal envelope in example 2;
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It is to be understood, however, that features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
A method of diagnosing a failure of a hub bearing as shown in figure 1.
Firstly, an original fault signal of an automobile hub bearing is obtained.
Thereafter, using waveletsAnd decomposing the original fault signal by packet decomposition to obtain a series of sub-signals, eliminating a lowest frequency component and reserving a high-frequency component containing fault information. Wavelet packet decomposition is to perform high-frequency decomposition and low-frequency decomposition on an original signal at the same time, and the process can be regarded as that the signal passes through a high-pass filter and a low-pass filter to obtain a high-frequency signal and a low-frequency signal. And decomposing the high-frequency signal and the low-frequency signal respectively, and then continuously decomposing the obtained low-frequency signal and high-frequency signal. With the increase of the decomposition scale, the decomposition of the wavelet packet on the signal is more and more fine, so that the characteristics of the signal in a certain frequency band can be obtained. The wavelet packet function is defined as:
Figure GDA0002523869640000051
j is a scale parameter, and the frequency is positioned; k is a translation parameter and positioning time; m is an oscillation parameter. From the wavelet packet function and the signal x (t), the wavelet packet coefficients:
Figure GDA0002523869640000052
representing the mth wavelet packet coefficient at the j-level decomposition scale.
Thus, the original signal x (t) can be rewritten as:
Figure GDA0002523869640000053
where N is the signal length. Wavelet packet decomposition altogether produces 2jWavelet packet coefficient Cj,mTheir length is N/2j. Wavelet packet coefficient of
Figure GDA0002523869640000054
And reconstructing to obtain the signal components of each node according to the wavelet packet coefficients.
And then, adding the sub-signals after wavelet packet decomposition into the original signals to perform empirical mode decomposition, decomposing the enhanced signals into components of different frequency bands in a self-adaptive manner, and reconstructing fault signals according to a kurtosis principle. As shown in fig. 2, the fault signal is further decomposed and reconstructed using wavelet packet enhanced empirical mode decomposition. The purpose of empirical mode decomposition is to separate the am signal from the original signal and to separate the complex signal into several eigenmode function components, i.e., IMF components. The empirical mode decomposition is different from the traditional signal processing method based on Fourier transform, does not need to set a basis function in advance, has self-adaptability and is suitable for processing non-stationary signals. However, empirical mode decomposition still has some inevitable drawbacks: 1. end-point effects; 2. mode aliasing. In this regard, the inventors propose wavelet packet enhanced empirical mode decomposition to improve the shortcomings of conventional empirical mode decomposition.
The principle of wavelet packet enhanced modal decomposition is to add a series of sub-signals decomposed by wavelet packets to an original signal, so that the sub-signals are uniformly distributed on the whole time-frequency space of the signal. At this time, signals of different scale features are adaptively spread to an appropriate reference scale. After multiple screening and averaging, the noises are mutually counteracted, and the signal keeps stable, so that the fault characteristics of the original signal are enhanced. Therefore, the original signal is preserved, and the modal aliasing phenomenon is weakened. The wavelet packet enhanced modal decomposition comprises the following steps:
step 1, adding a sub-signal w (j, m) of wavelet packet decomposition to the signal x (t), namely:
xm(t)＝x(t)+w(j,m)。
step 2, for the signal xm(t) performing empirical mode decomposition.
And 3, repeating the two steps m times, and adding a sub-signal once.
Step 4, averaging IMF components of each order obtained after m-time decomposition to obtain an overall average value Ij(t), the expression is as follows:
Figure GDA0002523869640000061
Ij(t) represents the jth IMF component, and Q represents the number of decompositions.
And (d) carrying out wavelet packet enhanced empirical mode decomposition on the signal x (t) to obtain a series of IMF components, and selecting proper components according to a kurtosis criterion to reconstruct to obtain a reconstructed signal y (t).
And then, performing Hilbert envelope analysis on the reconstructed signal obtained in the previous step to obtain an analysis result.
And finally, calculating a theoretical value of the bearing fault characteristic frequency, comparing the analysis result with the theoretical value to determine the fault type, and finally obtaining a fault conclusion. The theoretical value calculation method of the bearing fault characteristic frequency comprises a bearing fault outer ring characteristic frequency calculation method and a bearing inner ring fault characteristic frequency calculation method.
The bearing inner ring fault calculation formula is as follows:
Figure GDA0002523869640000071
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
The bearing outer ring fault calculation formula is as follows:
Figure GDA0002523869640000072
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
Hereinafter, in order to explain the application of the method to the fault diagnosis of the hub bearing outer ring, embodiment 1 is provided.
Example 1: wheel hub bearing outer ring fault diagnosis
A certain automobile hub bearing is of an SKF7018CD/P4A type, the diameter of a pitch circle is 115mm, the number of rolling elements is 20, the diameter of each rolling element is 12.5mm, and the contact angle is 15 degrees. The sampling frequency is 20000Hz, the bearing runs in no-load mode, and the rotating speed is 1200 r/min. According to a bearing fault outer ring characteristic frequency calculation formula:
Figure GDA0002523869640000073
calculating to obtain the outer ringThe fault signature frequency was 179 Hz.
The time domain diagram and the hilbert envelope spectrogram of the outer ring original signal are shown in fig. 3 and 4, and due to the interference of strong background noise, the impact matched with the outer ring fault characteristic frequency cannot be found in fig. 4, and the fault type cannot be judged. And (4) selecting db1 mother wavelets to perform 7-layer wavelet packet decomposition on the original signal to obtain 128 sub signals. The lowest frequency component containing a large amount of noise components is filtered, and the rest higher frequency components containing fault information are reserved. Then, the sub-signals are input into the original signals to further carry out wavelet packet enhanced empirical mode decomposition for 127 times, and 8-order IMF components are obtained. The energy of each order component is shown in fig. 5, and the component with the highest energy is selected, and the component contains obvious fault characteristics. The time domain graph and the Hilbert envelope spectrum are shown in FIGS. 6 and 7, and the impact 178.8Hz matched with the outer ring fault characteristic frequency, the double frequency 357.5Hz and the triple frequency 536.3Hz can be obviously observed from the graph. The experimental result that the fault characteristic frequency 178.8Hz is matched with the theoretical value 179Hz, so that the fault of the outer ring of the hub bearing can be accurately diagnosed.
Hereinafter, in order to explain the application of the method to the failure diagnosis of the inner ring of the hub bearing, embodiment 2 is provided.
Example 2: wheel hub bearing inner race fault diagnosis
A certain automobile hub bearing is of an SKF7018CD/P4A type, the diameter of a pitch circle is 115mm, the number of rolling elements is 20, the diameter of each rolling element is 12.5mm, and the contact angle is 15 degrees. The sampling frequency is 20000Hz, the bearing runs in no-load mode, and the rotating speed is 1200 r/min. The calculation formula of the fault characteristic frequency of the bearing inner ring is as follows:
Figure GDA0002523869640000081
and calculating to obtain the outer ring fault characteristic frequency of 220.9 Hz.
Time domain diagrams and Hilbert envelope spectrums of original signals of the inner ring are shown in fig. 8 and 9, and due to interference of strong noise, impact corresponding to the fault characteristic frequency of the inner ring cannot be found in fig. 9, so that the fault type cannot be judged. And (4) selecting db1 mother wavelet to decompose the original signal into 7-layer wavelet packets. The lowest frequency sub-signal containing a large amount of interference components is removed and the remaining components are retained. Then, 127 sub-signals are input into the original fault signal to further perform wavelet packet enhancement set empirical mode decomposition for 127 times, so as to obtain 8-order IMF components, and the energy of each component is shown in fig. 10. And selecting the IMF1 with the highest energy as a reconstruction signal, and performing envelope demodulation to obtain the impact containing obvious fault characteristics. The time domain graph and the Hilbert envelope spectrum are shown in FIGS. 11 and 12, and the impact 220.5Hz matched with the characteristic frequency of the inner ring fault and the double frequency 440.9Hz, triple frequency 661.4Hz and quadruple frequency 881.8Hz can be obviously observed from the graph. The fault characteristic frequency 220.5Hz of the experimental result is matched with the theoretical value 220.9Hz, so that the fault of the inner ring of the hub bearing is accurately diagnosed.
The above examples are provided to explain the inclusion of the invention, but the present invention is not limited to the above two embodiments. It is within the knowledge of a person skilled in the art to apply the invention also in various types of fault diagnosis.

Claims (5)

1. A method of diagnosing a failure of a hub bearing, comprising the steps of:
firstly, acquiring an original fault signal of an automobile hub bearing;
decomposing original fault signals: decomposing an original fault signal by utilizing wavelet packet decomposition to obtain a series of sub-signals, eliminating the lowest frequency component in the sub-signals, and reserving the high frequency component containing fault information;
thirdly, decomposing and reconstructing fault signals by utilizing a wavelet packet enhanced mode: adding the sub-signals in the step II into the original fault signal to carry out empirical mode decomposition, so as to decompose the sub-signals into components of different frequency bands in a self-adaptive manner, and reconstructing the fault signal according to a kurtosis principle;
analysis result: performing Hilbert envelope analysis on the reconstructed fault signal in the step three and calculating to obtain an analysis result;
diagnosing the fault type: calculating a theoretical value of the bearing fault characteristic frequency, comparing the theoretical value with the analysis result in the step (IV), and determining the fault type;
step IIThe wavelet packet decomposition is to perform high-frequency decomposition and low-frequency decomposition on an original signal at the same time to obtain a high-frequency signal and a low-frequency signal, and then continuously decompose the obtained high-frequency signal and the low-frequency signal, wherein a wavelet packet function is defined as:
Figure FDA0002655405560000011
j is a scale parameter for positioning frequency, k is a translation parameter for positioning time, and m is an oscillation parameter;
wavelet packet coefficient:
Figure FDA0002655405560000012
Figure FDA0002655405560000013
representing the mth wavelet packet coefficient under the j-layer decomposition scale;
original signal
Figure FDA0002655405560000014
N is the signal length;
wavelet packet decomposition altogether produces 2jWavelet packet coefficient Cj,mLength of N/2jThe wavelet packet coefficient equation is
Figure FDA0002655405560000015
2. A hub bearing failure diagnosis method according to claim 1, characterized in that: the wavelet packet enhanced modal decomposition comprises the following steps:
the method comprises the following steps: adding wavelet packet decomposed subsignals w (j, m), i.e. x, to the signal x (t)m(t)＝x(t)+w(j,m)；
Step two: for signal xm(t) performing empirical mode decomposition;
step three: repeating the process from the first step to the second step m times, and adding a sub-signal every time the process is repeated;
step four: decomposing for m timesAveraging the obtained eigenmode function components of each order to obtain an overall average value Ij(t)，
Figure FDA0002655405560000021
Ij(t) denotes the jth eigenmode function component, and Q denotes the number of decompositions.
3. A hub bearing failure diagnosis method according to claim 1, characterized in that: the theoretical value calculating method of the bearing fault characteristic frequency in the fifth step comprises a bearing fault outer ring characteristic frequency calculating method and a bearing fault inner ring characteristic frequency calculating method.
4. A hub bearing failure diagnosis method according to claim 1 or 3, characterized in that: the theoretical value calculation method of the bearing fault characteristic frequency in the fifth step comprises a bearing inner ring fault calculation formula, wherein the calculation formula is as follows:
Figure FDA0002655405560000022
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
5. A hub bearing failure diagnosis method according to claim 1 or 3, characterized in that: the theoretical value calculation method of the bearing fault characteristic frequency in the fifth step comprises a bearing outer ring fault calculation formula, wherein the calculation formula is as follows:
Figure FDA0002655405560000023
fⅠthe theoretical value of the characteristic frequency is shown, n represents the number of rolling elements, D represents the diameter of the rolling elements, D represents the diameter of the pitch circle, and alpha represents the bearing contact angle.
CN201811057836.XA 2018-09-11 2018-09-11 Wheel hub bearing fault diagnosis method Active CN109029996B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811057836.XA CN109029996B (en) 2018-09-11 2018-09-11 Wheel hub bearing fault diagnosis method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811057836.XA CN109029996B (en) 2018-09-11 2018-09-11 Wheel hub bearing fault diagnosis method

Publications (2)

Publication Number Publication Date
CN109029996A CN109029996A (en) 2018-12-18
CN109029996B true CN109029996B (en) 2021-01-26

Family

ID=64621102

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811057836.XA Active CN109029996B (en) 2018-09-11 2018-09-11 Wheel hub bearing fault diagnosis method

Country Status (1)

Country Link
CN (1) CN109029996B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110057583A (en) * 2019-03-01 2019-07-26 西人马(西安)测控科技有限公司 A kind of bearing fault recognition methods, device and computer equipment
CN110716532A (en) * 2019-10-17 2020-01-21 哈尔滨工程大学 Underwater robot propeller weak fault identification method based on wavelet packet energy and FFT
CN111189640A (en) * 2020-01-09 2020-05-22 珠海格力电器股份有限公司 Bearing fault monitoring method, monitoring device adopting same and washing machine
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
CN117635245A (en) * 2023-11-30 2024-03-01 广东电力交易中心有限责任公司 Power price prediction method and system based on multilevel frequency domain decomposition and IBM H optimization DELM

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102854015B (en) * 2012-10-15 2014-10-29 哈尔滨理工大学 Diagnosis method for fault position and performance degradation degree of rolling bearing
CN103995237A (en) * 2014-05-09 2014-08-20 南京航空航天大学 Satellite power supply system online fault diagnosis method
CN105784353A (en) * 2016-03-25 2016-07-20 上海电机学院 Fault diagnosis method for gear case of aerogenerator
CN106769052B (en) * 2017-03-21 2018-12-21 桂林电子科技大学 A kind of mechanical system rolling bearing intelligent failure diagnosis method based on clustering
CN107870892A (en) * 2017-07-01 2018-04-03 南京理工大学 A kind of municipal rail train Fault Diagnosis of Roller Bearings

Also Published As

Publication number Publication date
CN109029996A (en) 2018-12-18

Similar Documents

Publication Publication Date Title
CN109029996B (en) Wheel hub bearing fault diagnosis method
CN109883706B (en) Method for extracting local damage weak fault features of rolling bearing
JP6133112B2 (en) Rolling bearing diagnostic device and rolling bearing diagnostic method
CN111238813B (en) Method for extracting fault features of rolling bearing under strong interference
CN108801630B (en) Gear fault diagnosis method for single-channel blind source separation
CN108647667B (en) A kind of implementation method of the frequency domain amplitude spectrum kurtosis figure based on signal Time-frequency Decomposition
CN109187023B (en) Automobile generator bearing fault diagnosis method
CN100507490C (en) Large-sized low-speed heavy-loaded gear box fault diagnosis method
CN111855211B (en) Optimal demodulation frequency band determination method for fault feature enhancement
CN112183259A (en) Rolling bearing fault diagnosis method based on CEEMD and kurtosis weighted average threshold denoising
CN107525671B (en) Method for separating and identifying compound fault characteristics of transmission chain of wind turbine generator
CN112067297B (en) Bearing fault feature extraction method
CN116522074A (en) Rolling bearing signal noise reduction method based on adaptive window long time-frequency peak filtering
CN113865871A (en) Rolling bearing fault diagnosis method based on fault characteristic frequency multiplication energy judgment
CN113449630A (en) Bearing fault diagnosis method, system and medium for improving modulation double spectrum
CN109738189B (en) Rotating machinery frequency spectrum harmonic wave average optimization method
CN110147637B (en) Rub-impact fault diagnosis method based on wavelet and harmonic component greedy sparse identification
CN107490477A (en) The Fault Diagnosis of Gear Case method compared based on frequency spectrum kernel density function correlation
CN116150585A (en) Rotary machine fault diagnosis method based on product envelope spectrum
Huang et al. Short-time kurtogram for bearing fault feature extraction under time-varying speed conditions
CN114923689A (en) Rolling bearing fault diagnosis method based on local feature scale decomposition
CN114001961A (en) Rolling bearing fault feature targeted positioning and extracting method
CN114199569A (en) Fault diagnosis method for bearing and computer readable medium
CN111521396A (en) Bearing fault diagnosis method based on translation invariant high-density wavelet packet transformation
Damine et al. Comparative study between EEMD-MED and VMD-MED techniques dedicated to bearing fault detection in induction motors

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
GR01 Patent grant
GR01 Patent grant