AU2020103681A4 - Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value - Google Patents
Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value Download PDFInfo
- Publication number
- AU2020103681A4 AU2020103681A4 AU2020103681A AU2020103681A AU2020103681A4 AU 2020103681 A4 AU2020103681 A4 AU 2020103681A4 AU 2020103681 A AU2020103681 A AU 2020103681A AU 2020103681 A AU2020103681 A AU 2020103681A AU 2020103681 A4 AU2020103681 A4 AU 2020103681A4
- Authority
- AU
- Australia
- Prior art keywords
- mean value
- rolling bearing
- scale arrangement
- arrangement entropy
- ske
- 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.)
- Ceased
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005096 rolling process Methods 0.000 title claims abstract description 28
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 27
- 238000003745 diagnosis Methods 0.000 title claims abstract description 19
- 238000001228 spectrum Methods 0.000 claims abstract description 12
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 7
- 238000009826 distribution Methods 0.000 claims description 4
- 239000006185 dispersion Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000010892 electric spark Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Discrete Mathematics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a rolling bearing fault diagnosis method based on Fourier
decomposition and multi-scale arrangement entropy partial mean value, and belongs to the
technical field of equipment state monitoring and fault diagnosis. The method comprises the
following steps: acquiring an original rolling bearing fault vibration signal to be diagnosed;
decomposing the original rolling bearing fault vibration signal by using a Fourier
decomposition method; calculating a multi-scale arrangement entropy partial mean value of
each component; selecting the first three components with the largest multi-scale arrangement
entropy partial mean value for reconstruction; performing envelope spectrum analysis on the
reconstructed signal; and identifying fault characteristics according to an envelope spectrum.
According to the rolling bearing fault diagnosis method provided by the invention, the
complexity of each component is represented by the multi-scale arrangement entropy partial
mean value, the fault characteristic frequency and the frequency doubling thereof can be
effectively obtained, and the diagnosis effect is good.
1/4
Inputting original vibration signal
Decomposing signal by using FDM
Calculating PMMPE for each component
Selecting sensitive component for reconstruction
Performing envelope spectrum analysis
Obtaining a diagnostic result
Figure 1
Description
1/4
Inputting original vibration signal
Decomposing signal by using FDM
Calculating PMMPE for each component
Selecting sensitive component for reconstruction
Performing envelope spectrum analysis
Obtaining a diagnostic result
Figure 1
Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and
Multi-scale Arrangement Entropy Partial Mean Value
[01] The invention belongs to the technical field of equipment state monitoring and fault diagnosis, and particularly relates to a rolling bearing fault diagnosis method based on Fourier decomposition (FDM) and multi-scale arrangement entropy partial mean (PMMPE).
[02] Rolling bearing is an important component of many rotating machinery in modern industry, and is also one of the most vulnerable components in the machine. According to statistics, 30% of the faults of rotating machinery are caused by bearing. Once the rolling bearing fails, the normal operation of the equipment can be influenced, so that the machine can generate violent vibration and interference noise, even great economic loss and large safety accidents can be caused. Therefore, the research of rolling bearing state monitoring and fault diagnosis has important engineering application value and theoretical significance for ensuring the stable operation of equipment and the safe production of enterprises.
[03] Due to the time-frequency joint distribution information of nonlinear and non-stationary signals are provided, the time-frequency analysis method has been widely used in the field of fault diagnosis of bearings, gears, etc., but the typical Empirical Mode Decomposition (EMD) and wavelet analysis all have inherent flaws. EMD can adaptively decompose non-stationary vibration signals into several inherent modal functions distributed from high frequency to low frequency. Because EMD is a data-driven signal decomposition method, which avoids the choice of basic functions, and has been affirmed and studied by many scholars. Although EMD has been applied in the field of mechanical failure, the inherent end effect, modal aliasing and other defects limit popularization and application of EMD. The basic idea of wavelet analysis is to use a wavelet function with adjustable time window width to replace the window function in the short-time Fourier transform so that different positions in the time frequency plane have different resolutions. However, the main problem of wavelet analysis is the need to select the wavelet basis and the number of decomposition layers in advance.
[04] Aiming at the defects of the existing time-frequency analysis method, the invention provides a rolling bearing fault diagnosis method based on Fourier decomposition and multi-scale arrangement entropy partial mean value. According to the method provided by the invention, aliasing among frequency domain components can be solved; and components capable of representing fault information to the maximum extent are selected through multi-scale arrangement entropy partial mean value, and diagnosis is carried out from an envelope spectrum. The method provided by the invention can effectively extract components containing rich fault information and has better diagnosis effect.
[05] The invention provides a rolling bearing fault diagnosis method based on Fourier decomposition and multi-scale arrangement entropy partial mean value, which comprises the following steps:
[06] (1) acquiring an original rolling bearing fault vibration signal to be diagnosed;
[07] (2) decomposing the fault vibration signal of the original rolling bearing by utilizing a Fourier decomposition method to obtain components;
[08] (3) calculating the partial mean value of the multi-scale arrangement entropy of each component;
[09] (4) selecting the first three components with the largest partial mean value of the multi-scale arrangement entropy for reconstruction to obtain a reconstructed signal;
[010] (5) carrying out envelope spectrum analysis on the reconstructed signal to obtain an envelope spectrum;
[011] (6) identifying fault characteristics according to the envelope spectrum.
[012] The specific steps for carrying out Fourier decomposition on the fault vibration signal of the original rolling bearing in the step (2) are as follows:
[013] The Fourier decomposition method can adaptively decompose an original signal into a plurality of Fourier inherent band functions and a sum of residual terms, wherein the Fourier inherent band functions satisfy the following conditions:
[014] 1) The Fourier inherent band function has zero mean, i. e.:
[015] 2) Any two Fourier inherent band functions are mutually orthogonal, i. e.:
[016] 3) Analytic function form of Fourier inherent band function: The Fourier inherent band function has non-negative instantaneous amplitude and instantaneous frequency, that is, a i (t);> 0, where y i (t) C oo [a, b]. Therefore, the Fourier inherent band function is the sum of the zero mean sine functions with successive bands.
[017] On the basis of defining a Fourier inherent band function, the Fourier decomposition method (FDM) steps are as follows:
[018] 1) carrying out fast Fourier transform on a complex signal x (n), namely X
[k]=FFT { [x (n) ] };
[019] 2) performing forward search, i. e. scanning the analytic function of Fourier inherent band function from low frequency to high frequency
[020] In order to obtain an analytic function of a minimum number of Fourier inherent band functions from a low frequency to a high frequency scanning, for each i=1, 2,... , M, starting from N i-1+1, increasing gradually until the maximum N i is reached, where N 0=0, N M=(N/2-1), satisfying: N i-1+1 <=N i <=(N/2-1), and:
[021] Similarly, it is possible to perform a reverse search, i. e., scanning the analytic function component of the Fourier inherent band function from high frequency to low frequency, accordingly, the upper and lower sum limits of Equation (2) are changed from N i to N i-I -1, where i=1, 2,... , M, N 0=N/2, N M=1, the search should start with N i-I -1, gradually decrease until the smallest N i is reached, satisfying: 1 <N i<=Ni-1-1-1, and the phase is a monotonically increasing function.
[022] Calculating the partial mean value of the multi-scale arrangement entropy of each component in the step (3), specifically as follows:
[023] The absolute value of skewness a' correspondingly reflects the degree of skewness. The skewness is driven by the original data, so it is not suitable to compare the degree of skewness of different data; in order to compare the skewness of different data, calculate the relative value of skewness, namely skewness: Skewness, Ske; skewness is the deviation of the arithmetic mean and the mode in standard deviation, so its value range is generally between 0 and 3; The Ske of 0 means symmetrical distribution, and the Ske of +3 and -3 means extreme right skewness and extreme left skewness, respectively;
[024] Wherein SD represents the standard deviation of the original data;
[025] According to the definition of the skewness and the relation between the skewness and the mean value, the multi-scale arrangement entropy partial mean value PMMPE is defined as follows:
[026] PMMPE=(1+|Ske(MPE)|/3)*mean(MPE) (4)
[027] In the formula, Ske (MDE) and mean (MDE) represent the skewness and mean of multi-scale dispersion entropy over - scales, respectively.
[028] According to the method, the traditional Fourier representation method of the constant amplitude and the constant frequency is expanded to the generalized Fourier representation of the time amplitude and the time-varying frequency, and the method has completeness, orthogonality, locality and self-adaptability; the multi-scale arrangement entropy partial mean value is a new complexity quantitative index, and the complexity of components can be characterized; and the method can effectively obtain the fault characteristic frequency and the frequency doubling thereof, and accurately diagnose the vibration signal of the rolling bearing.
[029] Figure 1 is a schematic flow chart of the method according to the present invention;
[030] Figure 2 is a time domain waveform diagram of a fault vibration signal of an inner ring of a rolling bearing according to the present invention;
[031] Figure 3 is a component result obtained by decomposing a vibration signal with the Fourier decomposition method according to the present invention;
[032] Figure 4 is a graph showing the variation trend of multi-scale arrangement entropy partial means according to the present invention;
[033] Figure 5 is an envelope spectrum of the diagnosis of the method according to the invention.
[034] In order that the objects, technical schemes and advantages of the embodiments of the present invention will be more apparent, the technical schemes in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention.
[035] Referring to Figure 1, the rolling bearing fault diagnosis method based on Fourier decomposition and multi-scale arrangement entropy partial mean value in the embodiment comprises the following steps:
[036] (1) acquiring an original rolling bearing fault vibration signal to be diagnosed;
[037] (2) decomposing the fault vibration signal of the original rolling bearing by utilizing a Fourier decomposition method to obtain components;
[038] (3) calculating the partial mean value of the multi-scale arrangement entropy of each component;
[039] (4) selecting the first three components with the largest partial mean value of the multi-scale arrangement entropy for reconstruction;
[040] (5) carrying out envelope spectrum analysis on the reconstructed signal to obtain an envelope spectrum;
[041] (6) identifying fault characteristics according to the envelope spectrum.
[042] The method for diagnosing the fault of the rolling bearing by the Fourier decomposition and the multi-scale arrangement entropy partial mean value provided by the embodiment has the advantages of creativity in the aspect of representing the complexity of each component after decomposition, and has good recognition effect in the aspect of recognizing fault characteristic information in an envelope spectrum.
[043] The specific steps for carrying out Fourier decomposition on the fault vibration signal of the original rolling bearing in the step (2) are as follows:
[044] The Fourier decomposition method is capable of adaptively decomposing an original signal into a sum of a plurality of Fourier inherent band functions (FIBF) and a residual term, wherein the FIBF satisfy the following conditions:
[045] 1) FIBF has zero mean, i. e.:
[046] 2) Any two FIBF are mutually orthogonal, i. e.:
[047] 3) The analytic function form of FIBF (Analytic FIBF, AFIBF): the analytic function form has non-negative instantaneous amplitude and instantaneous frequency, that is, a i (t);> 0, where y i (t) E C oo [a, b]. Therefore, the FIBF is the sum of the zero mean sine functions with successive frequency bands.
[048] On the basis of defining FIBF, the Fourier decomposition method (FDM) steps are as follows:
[049] 1) carrying out fast Fourier transform on a complex signal x (n), namely X
[k]=FFT { [x (n) ] };
[050] 2) performing forward searching, i. e. scanning the AFIBF from low frequency to high frequency
[051] In order to obtain a minimum number of AFIBF from low frequency to high frequency scanning, for each i=1, 2,... , M, starting from N i-1+1, increasing gradually until the maximum N i is reached, where N 0=0, N M=(N/2-1), satisfying: N i-1+1 <N i <=(N/2-1), and:
[052] Similarly, it is possible to perform a reverse search, that is, scanning the AFIBF component from high frequency to low frequency, accordingly, the upper and lower sum limits in Equation (2) are changed from N i to N i-I-1, where i=1, 2,... , M, N 0=N/2, N M=1, the search should start with N i-I-1, gradually decrease until the smallest N i is reached, satisfying: 1 N i <=N i-I-I-1, and the phase is a monotonically increasing function.
[053] The step of calculating the partial mean value of the multi-scale arrangement entropy of the components in the step (3) is as follows:
[054] The absolute value of the skewness a' correspondingly reflects the degree of skewness. However, the skewness is driven by original data, so it is not suitable to compare the degree of skewness of different data. In order to compare the skewness of different data, calculate the relative value of the skewness, that is, the Skewness. The skewness is the deviation between the arithmetic mean and the mode in standard deviation, so its value range is generally between 0 and 3. The Ske of 0 indicates a symmetrical distribution, and the Ske of +3 and -3 indicates extreme right skewness and extreme left skewness, respectively.
[055] Where SD represents the standard deviation of the original data.
[056] According to the definition of the skewness and the relation between the skewness and the mean value, the definition of the PMMPE index provided by the invention is as follows:
[057] PMMPE=(1+|Ske(MPE)|/3)*mean(MPE) (4)
[058] In the formula, the Ske (MDE) and mean (MDE) represents the skewness and mean of multi-scale dispersion entropy over - scales, respectively.
[059] And selecting the first three components with the largest partial mean value of the multi-scale arrangement entropy for reconstruction, and performing envelope spectrum analysis on the reconstructed signal to obtain an envelope spectrum containing fault characteristic information.
[060] In order to verify the effectiveness of this method in fault diagnosis of the rolling bearing, the signal is effectively decomposed by the Fourier decomposition method, the complexity of each component is analyzed, and the effectiveness of the method is illustrated.
[061] The experimental equipment adopted in the embodiment is BVT-5 bearing vibration measuring instrument, the type of the test bearing is 6210 deep groove ball bearing, and the single point fault is arranged on the bearing by using the electric spark machining technology. After the experimental bearing is installed, the outer ring of the bearing is fixed through the radial loader and the axial loader, the outer ring is kept fixed all the time in the experimental process, and the inner ring rotates synchronously with the main shaft. The rotating speed is 1800r/min, the corresponding rotating frequency f r=30Hz, and signals are collected by an acceleration sensor, wherein the axial vibration signal is collected by a No.2 sensor, and the radial vibration signal is collected by a No.3 sensor.
[062] In the embodiment, a group of data of the fault of the inner ring of the rolling bearing is selected, the sampling frequency is 10240Hz, the characteristic frequency f i of the fault of the inner ring is calculated to be 177.21Hz, and the time domain waveform diagram is shown in Figure 2.
[063] First, performing FDM decomposition on the bearing inner ring fault vibration signal to obtain 31 components and one residual component r as shown in Figure 3, calculating a multi-scale arrangement entropy partial mean value for each decomposed component to obtain a variation trend of the partial mean value as shown in Figure 4, and extracting a sensitive component containing rich fault information according to the magnitude of the value, in which the partial mean value of the 24th, 28th and 29th components is the first three largest partial mean values. After the three components are added and reconstructed, an envelope spectrum is drawn as shown in Figure 5, and it can be seen from the figure that there is an obvious peak value at the fault characteristic frequency f i, the 2f i and 3f i just correspond to the 2 frequency and the 3 frequency multiplication of the fault characteristic frequency, and modulation side bands with intervals equal to the rotation frequency f r of 30Hz are arranged on the two sides of each frequency multiplication, which accords with the fault characteristics of the inner ring, so that the fault can be effectively diagnosed as the fault of the inner ring of the bearing.
[064] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms, in keeping with the broad principles and the spirit of the invention described herein.
[065] The present invention and the described embodiments specifically include the best method known to the applicant of performing the invention. The present invention and the described preferred embodiments specifically include at least one feature that is industrially applicable
Claims (2)
1. The rolling bearing fault diagnosis method based on Fourier decomposition and multi-scale arrangement entropy partial mean value, characterized in that the method comprises the following steps:
(1) acquiring an original rolling bearing fault vibration signal to be diagnosed;
(2) decomposing the original rolling bearing fault vibration signal by using a Fourier decomposition method to obtain components;
(3) calculating the partial mean value of the multi-scale arrangement entropy of each component;
(4) selecting the first three components with the largest partial mean value of the multi-scale arrangement entropy for reconstruction to obtain a reconstructed signal;
(5) carrying out envelope spectrum analysis on the reconstructed signal to obtain an envelope spectrum;
(6) identifying fault characteristics according to the envelope spectrum.
2. The rolling bearing fault diagnosis method based on Fourier decomposition and multi-scale arrangement entropy partial mean value, according to claim 1, characterized in that the step (3) is as follows:
Skew degree: Skewness, Ske is the deviation between arithmetic mean and mode in unit of standard deviation, so the value range of Ske is generally between 0 and 3; Ske is 0 to represent symmetric distribution, Ske is +3 to represent extreme right deviation and Ske is -3 to represent extreme left deviation respectively;
Wherein SD represents the standard deviation of the original data;
According to the definition of the skewness and the relation between the skewness and the mean value, the multi-scale arrangement entropy partial mean value PMMPE is defined as follows:
PMMPE=(1+|Ske(MPE)|/3)*mean(MPE) (4)
In the formula, Ske (MDE) and mean (MDE) represent the skewness and mean value of multi-scale dispersion entropy over - scales, respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020103681A AU2020103681A4 (en) | 2020-11-26 | 2020-11-26 | Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2020103681A AU2020103681A4 (en) | 2020-11-26 | 2020-11-26 | Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2020103681A4 true AU2020103681A4 (en) | 2021-02-04 |
Family
ID=74236405
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2020103681A Ceased AU2020103681A4 (en) | 2020-11-26 | 2020-11-26 | Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2020103681A4 (en) |
Cited By (38)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113029567A (en) * | 2021-02-26 | 2021-06-25 | 上海电机学院 | Fault signal diagnosis method based on feature fusion equipotential planet map |
| CN113048220A (en) * | 2021-03-12 | 2021-06-29 | 中煤科工集团重庆研究院有限公司 | Mining elevator gear box hidden danger identification method and monitoring device |
| CN113191245A (en) * | 2021-04-25 | 2021-07-30 | 西安交通大学 | Migration intelligent diagnosis method for multi-source rolling bearing health state fusion |
| CN113189457A (en) * | 2021-04-26 | 2021-07-30 | 天津大学 | Multi-scale feature extraction technology based on partial discharge original time domain waveform |
| CN113295420A (en) * | 2021-06-10 | 2021-08-24 | 燕山大学 | Rolling bearing fault diagnosis method and system based on period guidance group sparse model |
| CN113340547A (en) * | 2021-05-31 | 2021-09-03 | 中国矿业大学 | Winch vibration diagnosis method based on improved lumped mean-square decomposition |
| CN113468688A (en) * | 2021-07-05 | 2021-10-01 | 西安交通大学 | Bearing fault diagnosis method based on parameter optimization VMD and weighted Gini index |
| CN113503960A (en) * | 2021-07-21 | 2021-10-15 | 美核电气（济南）股份有限公司 | Mechanical system fault diagnosis method and system based on visual image |
| CN113567128A (en) * | 2021-07-26 | 2021-10-29 | 西南交通大学 | Method, equipment and storage medium for precisely extracting and diagnosing fault characteristics of train bearing |
| CN113591248A (en) * | 2021-08-09 | 2021-11-02 | 兰州理工大学 | Bearing fault diagnosis method in mine hoist transmission part |
| CN113776820A (en) * | 2021-09-13 | 2021-12-10 | 武汉科技大学 | Gear fault diagnosis method based on VMD and symbol sequence entropy analysis |
| CN113822193A (en) * | 2021-09-18 | 2021-12-21 | 成都运达科技股份有限公司 | Rolling bearing fault diagnosis method and system based on narrow-band enhanced envelope spectrum |
| CN113837071A (en) * | 2021-09-23 | 2021-12-24 | 重庆大学 | Partial migration fault diagnosis method based on multi-scale weight selection countermeasure network |
| CN114001961A (en) * | 2021-10-29 | 2022-02-01 | 苏州微著设备诊断技术有限公司 | Rolling bearing fault feature targeted positioning and extracting method |
| CN114018581A (en) * | 2021-11-08 | 2022-02-08 | 中国航发哈尔滨轴承有限公司 | CEEMDAN-based rolling bearing vibration signal decomposition method |
| CN114061746A (en) * | 2021-11-23 | 2022-02-18 | 洛阳师范学院 | Repeated transient signal extraction method in fault diagnosis of rotary machine |
| WO2022037068A1 (en) * | 2021-03-22 | 2022-02-24 | 南京航空航天大学 | Method for diagnosis of fault in machine tool bearing |
| CN114088400A (en) * | 2021-11-01 | 2022-02-25 | 中国人民解放军92728部队 | Rolling bearing fault diagnosis method based on envelope permutation entropy |
| CN114323649A (en) * | 2021-12-31 | 2022-04-12 | 福州大学 | Slewing bearing fault diagnosis method based on CEEMDAN and PSO-MOMEDA |
| CN114427972A (en) * | 2022-02-09 | 2022-05-03 | 中国人民解放军战略支援部队航天工程大学士官学校 | Rolling bearing performance degradation feature extraction method and system |
| CN114486263A (en) * | 2022-02-15 | 2022-05-13 | 浙江大学 | Noise reduction and demodulation method for vibration signal of rolling bearing of rotary machine |
| CN114486252A (en) * | 2022-01-28 | 2022-05-13 | 西北工业大学 | Rolling bearing fault diagnosis method based on vector modulus maximum envelope |
| CN114526915A (en) * | 2022-01-21 | 2022-05-24 | 华南理工大学 | Early fault time point identification method based on rolling bearing characteristic distribution |
| CN114742111A (en) * | 2022-05-24 | 2022-07-12 | 南京林业大学 | Fault diagnosis method and system based on parameter adaptive characteristic modal decomposition |
| CN114757060A (en) * | 2022-06-15 | 2022-07-15 | 石家庄铁道大学 | Bearing fault diagnosis method based on morphological function construction scale space |
| CN114841939A (en) * | 2022-04-24 | 2022-08-02 | 启东市鸿盛纺织有限公司 | Grey cloth defect degree analysis method based on image processing |
| CN114858467A (en) * | 2022-05-26 | 2022-08-05 | 上海交通大学 | Anti-noise and cross-noise-domain misfire diagnosis method and system for diesel engine |
| CN114881072A (en) * | 2022-04-15 | 2022-08-09 | 东北林业大学 | Fourier decomposition signal noise reduction method based on peak envelope spectrum |
| CN115031967A (en) * | 2022-05-27 | 2022-09-09 | 北京天玛智控科技股份有限公司 | Fault diagnosis method and device for rolling bearing and electronic equipment |
| CN115169417A (en) * | 2022-08-02 | 2022-10-11 | 江苏利核仪控技术有限公司 | Rolling bearing fault feature extraction method based on skewness maximization |
| CN115219199A (en) * | 2022-07-14 | 2022-10-21 | 天津职业技术师范大学 | Bearing weak fault extraction method based on depth-related entropy spectral density |
| CN115266094A (en) * | 2022-07-03 | 2022-11-01 | 三峡大学 | Rolling bearing fault diagnosis method based on TVD and MED |
| CN116625685A (en) * | 2023-04-20 | 2023-08-22 | 广东海洋大学 | Rolling bearing combination fault diagnosis method based on multi-domain feature construction |
| CN117056671A (en) * | 2023-08-14 | 2023-11-14 | 上海如海光电科技有限公司 | EMD-based Raman spectrum noise reduction method |
| CN117629636A (en) * | 2023-12-05 | 2024-03-01 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
| CN117723303A (en) * | 2024-02-01 | 2024-03-19 | 湘潭大学 | Acoustic monitoring method for wind generating set bearing |
| CN118171147A (en) * | 2024-03-27 | 2024-06-11 | 哈尔滨理工大学 | Rolling bearing small sample fault diagnosis method based on twin multi-scale residual error network |
| CN118347729A (en) * | 2024-04-23 | 2024-07-16 | 广东派勒智能纳米科技股份有限公司 | Rotor fault diagnosis method, system and medium based on two-dimensional hologram |
-
2020
- 2020-11-26 AU AU2020103681A patent/AU2020103681A4/en not_active Ceased
Cited By (58)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113029567A (en) * | 2021-02-26 | 2021-06-25 | 上海电机学院 | Fault signal diagnosis method based on feature fusion equipotential planet map |
| CN113048220A (en) * | 2021-03-12 | 2021-06-29 | 中煤科工集团重庆研究院有限公司 | Mining elevator gear box hidden danger identification method and monitoring device |
| CN113048220B (en) * | 2021-03-12 | 2022-11-08 | 中煤科工集团重庆研究院有限公司 | Mining elevator gear box hidden danger identification method and monitoring device |
| WO2022037068A1 (en) * | 2021-03-22 | 2022-02-24 | 南京航空航天大学 | Method for diagnosis of fault in machine tool bearing |
| CN113191245A (en) * | 2021-04-25 | 2021-07-30 | 西安交通大学 | Migration intelligent diagnosis method for multi-source rolling bearing health state fusion |
| CN113189457A (en) * | 2021-04-26 | 2021-07-30 | 天津大学 | Multi-scale feature extraction technology based on partial discharge original time domain waveform |
| CN113340547A (en) * | 2021-05-31 | 2021-09-03 | 中国矿业大学 | Winch vibration diagnosis method based on improved lumped mean-square decomposition |
| CN113295420A (en) * | 2021-06-10 | 2021-08-24 | 燕山大学 | Rolling bearing fault diagnosis method and system based on period guidance group sparse model |
| CN113295420B (en) * | 2021-06-10 | 2022-04-08 | 燕山大学 | Rolling bearing fault diagnosis method and system based on period guidance group sparse model |
| CN113468688B (en) * | 2021-07-05 | 2022-12-09 | 西安交通大学 | Bearing fault diagnosis method based on parameter optimization VMD and weighted Gini index |
| CN113468688A (en) * | 2021-07-05 | 2021-10-01 | 西安交通大学 | Bearing fault diagnosis method based on parameter optimization VMD and weighted Gini index |
| CN113503960A (en) * | 2021-07-21 | 2021-10-15 | 美核电气（济南）股份有限公司 | Mechanical system fault diagnosis method and system based on visual image |
| CN113567128A (en) * | 2021-07-26 | 2021-10-29 | 西南交通大学 | Method, equipment and storage medium for precisely extracting and diagnosing fault characteristics of train bearing |
| CN113591248A (en) * | 2021-08-09 | 2021-11-02 | 兰州理工大学 | Bearing fault diagnosis method in mine hoist transmission part |
| CN113776820B (en) * | 2021-09-13 | 2024-03-29 | 武汉科技大学 | Gear fault diagnosis method based on VMD and symbol sequence entropy analysis |
| CN113776820A (en) * | 2021-09-13 | 2021-12-10 | 武汉科技大学 | Gear fault diagnosis method based on VMD and symbol sequence entropy analysis |
| CN113822193A (en) * | 2021-09-18 | 2021-12-21 | 成都运达科技股份有限公司 | Rolling bearing fault diagnosis method and system based on narrow-band enhanced envelope spectrum |
| CN113837071A (en) * | 2021-09-23 | 2021-12-24 | 重庆大学 | Partial migration fault diagnosis method based on multi-scale weight selection countermeasure network |
| CN113837071B (en) * | 2021-09-23 | 2024-02-02 | 重庆大学 | Partial migration fault diagnosis method based on multiscale weight selection countermeasure network |
| CN114001961B (en) * | 2021-10-29 | 2024-04-23 | 苏州微著设备诊断技术有限公司 | Rolling bearing fault feature targeted positioning and extracting method |
| CN114001961A (en) * | 2021-10-29 | 2022-02-01 | 苏州微著设备诊断技术有限公司 | Rolling bearing fault feature targeted positioning and extracting method |
| CN114088400A (en) * | 2021-11-01 | 2022-02-25 | 中国人民解放军92728部队 | Rolling bearing fault diagnosis method based on envelope permutation entropy |
| CN114088400B (en) * | 2021-11-01 | 2024-04-09 | 中国人民解放军92728部队 | Rolling bearing fault diagnosis method based on envelope permutation entropy |
| CN114018581A (en) * | 2021-11-08 | 2022-02-08 | 中国航发哈尔滨轴承有限公司 | CEEMDAN-based rolling bearing vibration signal decomposition method |
| CN114018581B (en) * | 2021-11-08 | 2024-04-16 | 中国航发哈尔滨轴承有限公司 | Rolling bearing vibration signal decomposition method based on CEEMDAN |
| CN114061746B (en) * | 2021-11-23 | 2023-06-27 | 洛阳师范学院 | Repeated transient signal extraction method in rotary machinery fault diagnosis |
| CN114061746A (en) * | 2021-11-23 | 2022-02-18 | 洛阳师范学院 | Repeated transient signal extraction method in fault diagnosis of rotary machine |
| CN114323649A (en) * | 2021-12-31 | 2022-04-12 | 福州大学 | Slewing bearing fault diagnosis method based on CEEMDAN and PSO-MOMEDA |
| CN114526915B (en) * | 2022-01-21 | 2022-12-16 | 华南理工大学 | Early fault time point identification method based on rolling bearing characteristic distribution |
| CN114526915A (en) * | 2022-01-21 | 2022-05-24 | 华南理工大学 | Early fault time point identification method based on rolling bearing characteristic distribution |
| CN114486252A (en) * | 2022-01-28 | 2022-05-13 | 西北工业大学 | Rolling bearing fault diagnosis method based on vector modulus maximum envelope |
| CN114486252B (en) * | 2022-01-28 | 2023-06-30 | 西北工业大学 | Rolling bearing fault diagnosis method of vector mode maximum envelope |
| CN114427972A (en) * | 2022-02-09 | 2022-05-03 | 中国人民解放军战略支援部队航天工程大学士官学校 | Rolling bearing performance degradation feature extraction method and system |
| CN114486263A (en) * | 2022-02-15 | 2022-05-13 | 浙江大学 | Noise reduction and demodulation method for vibration signal of rolling bearing of rotary machine |
| CN114881072A (en) * | 2022-04-15 | 2022-08-09 | 东北林业大学 | Fourier decomposition signal noise reduction method based on peak envelope spectrum |
| CN114881072B (en) * | 2022-04-15 | 2024-07-19 | 东北林业大学 | Fourier decomposition signal noise reduction method based on peak envelope spectrum |
| CN114841939B (en) * | 2022-04-24 | 2024-05-24 | 杭州丰越丝绸织造有限公司 | Grey cloth defect degree analysis method based on image processing |
| CN114841939A (en) * | 2022-04-24 | 2022-08-02 | 启东市鸿盛纺织有限公司 | Grey cloth defect degree analysis method based on image processing |
| CN114742111A (en) * | 2022-05-24 | 2022-07-12 | 南京林业大学 | Fault diagnosis method and system based on parameter adaptive characteristic modal decomposition |
| CN114742111B (en) * | 2022-05-24 | 2023-04-07 | 南京林业大学 | Fault diagnosis method and system based on parameter adaptive characteristic modal decomposition |
| CN114858467A (en) * | 2022-05-26 | 2022-08-05 | 上海交通大学 | Anti-noise and cross-noise-domain misfire diagnosis method and system for diesel engine |
| CN115031967A (en) * | 2022-05-27 | 2022-09-09 | 北京天玛智控科技股份有限公司 | Fault diagnosis method and device for rolling bearing and electronic equipment |
| CN114757060A (en) * | 2022-06-15 | 2022-07-15 | 石家庄铁道大学 | Bearing fault diagnosis method based on morphological function construction scale space |
| CN115266094B (en) * | 2022-07-03 | 2024-09-17 | 三峡大学 | Rolling bearing fault diagnosis method based on TVD and MED |
| CN115266094A (en) * | 2022-07-03 | 2022-11-01 | 三峡大学 | Rolling bearing fault diagnosis method based on TVD and MED |
| CN115219199A (en) * | 2022-07-14 | 2022-10-21 | 天津职业技术师范大学 | Bearing weak fault extraction method based on depth-related entropy spectral density |
| CN115219199B (en) * | 2022-07-14 | 2024-07-16 | 天津职业技术师范大学 | Method for extracting weak faults of bearing based on depth-dependent entropy spectrum density |
| CN115169417A (en) * | 2022-08-02 | 2022-10-11 | 江苏利核仪控技术有限公司 | Rolling bearing fault feature extraction method based on skewness maximization |
| CN115169417B (en) * | 2022-08-02 | 2023-11-03 | 江苏利核仪控技术有限公司 | Rolling bearing fault feature extraction method based on deflection maximization |
| CN116625685A (en) * | 2023-04-20 | 2023-08-22 | 广东海洋大学 | Rolling bearing combination fault diagnosis method based on multi-domain feature construction |
| CN116625685B (en) * | 2023-04-20 | 2023-12-15 | 广东海洋大学 | Rolling bearing combination fault diagnosis method based on multi-domain feature construction |
| CN117056671A (en) * | 2023-08-14 | 2023-11-14 | 上海如海光电科技有限公司 | EMD-based Raman spectrum noise reduction method |
| CN117629636A (en) * | 2023-12-05 | 2024-03-01 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
| CN117629636B (en) * | 2023-12-05 | 2024-05-24 | 哈尔滨工程大学 | Health assessment and fault diagnosis method and system for rolling bearing of combustion engine |
| CN117723303A (en) * | 2024-02-01 | 2024-03-19 | 湘潭大学 | Acoustic monitoring method for wind generating set bearing |
| CN117723303B (en) * | 2024-02-01 | 2024-05-10 | 湘潭大学 | Acoustic monitoring method for wind generating set bearing |
| CN118171147A (en) * | 2024-03-27 | 2024-06-11 | 哈尔滨理工大学 | Rolling bearing small sample fault diagnosis method based on twin multi-scale residual error network |
| CN118347729A (en) * | 2024-04-23 | 2024-07-16 | 广东派勒智能纳米科技股份有限公司 | Rotor fault diagnosis method, system and medium based on two-dimensional hologram |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2020103681A4 (en) | Rolling Bearing Fault Diagnosis Method Based on Fourier Decomposition and Multi-scale Arrangement Entropy Partial Mean Value | |
| Qin | A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis | |
| Hoseinzadeh et al. | Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition | |
| CN111089726B (en) | Rolling bearing fault diagnosis method based on optimal dimension singular spectrum decomposition | |
| Boudiaf et al. | A summary of vibration analysis techniques for fault detection and diagnosis in bearing | |
| CN109932179B (en) | Rolling bearing fault detection method based on DS self-adaptive spectrum reconstruction | |
| CN114705426A (en) | Early fault diagnosis method for rolling bearing | |
| Dou et al. | A rule-based intelligent method for fault diagnosis of rotating machinery | |
| CN109765055B (en) | Rolling bearing fault detection method and system based on EWT, spectrum effective value and KNN | |
| Luo et al. | Cyclic harmonic ratio defined in squared envelope spectrum and log-envelope spectrum for gearbox fault diagnosis | |
| Xu et al. | Detecting weak position fluctuations from encoder signal using singular spectrum analysis | |
| CN113654798A (en) | Fault diagnosis method and device and electronic equipment | |
| Guo et al. | An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression | |
| CN112487882B (en) | Method for generating non-sparse index-guided enhanced envelope spectrum based on spectrum coherence | |
| Zheng et al. | An improved empirical wavelet transform and refined composite multiscale dispersion entropy-based fault diagnosis method for rolling bearing | |
| Lu et al. | Bearing fault diagnosis based on clustering and sparse representation in frequency domain | |
| CN111365251A (en) | Intelligent diagnosis method for faults of centrifugal pump unit | |
| Liu et al. | Asymmetric penalty sparse model based cepstrum analysis for bearing fault detections | |
| Shi et al. | A dual-guided adaptive decomposition method of fault information and fault sensitivity for multi-component fault diagnosis under varying speeds | |
| Alabied et al. | Fault diagnosis of centrifugal pumps based on the intrinsic time-scale decomposition of motor current signals | |
| CN110866519B (en) | Rolling bearing fault diagnosis method based on Fourier decomposition and multiscale permutation entropy deviation value | |
| CN113899548A (en) | Rolling bearing fault diagnosis method based on harmonic kurtosis spectrum | |
| CN117686232A (en) | Method, device and storage medium for extracting vibration fundamental frequency of gas turbine in real time | |
| CN117109923A (en) | Rolling bearing fault diagnosis method and system | |
| CN116150585A (en) | Rotary machine fault diagnosis method based on product envelope spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |