CN114441172B - Rolling bearing fault vibration signal analysis method - Google Patents
Rolling bearing fault vibration signal analysis method Download PDFInfo
- Publication number
- CN114441172B CN114441172B CN202111627847.9A CN202111627847A CN114441172B CN 114441172 B CN114441172 B CN 114441172B CN 202111627847 A CN202111627847 A CN 202111627847A CN 114441172 B CN114441172 B CN 114441172B
- Authority
- CN
- China
- Prior art keywords
- filter
- signal
- fault
- particle
- optimal
- 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
Links
- 238000005096 rolling process Methods 0.000 title claims abstract description 31
- 238000004458 analytical method Methods 0.000 title claims abstract description 21
- 239000002245 particle Substances 0.000 claims abstract description 72
- 238000001228 spectrum Methods 0.000 claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 25
- 238000001914 filtration Methods 0.000 claims abstract description 10
- 230000006870 function Effects 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 11
- 238000005457 optimization Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000011478 gradient descent method Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000001052 transient effect Effects 0.000 description 3
- 125000003275 alpha amino acid group Chemical group 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training 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
- G06F17/15—Correlation function computation including computation of convolution operations
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Acoustics & Sound (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The application relates to a rolling bearing fault vibration signal analysis method, which comprises the following steps: s1, obtaining an original vibration signal, and calculating a characteristic frequency; s2, initializing parameters of a particle swarm algorithm, and setting a range of the length of the optimizing filter and the iteration times; s3, initializing a filter, and filtering the vibration signal by adopting blind deconvolution based on square envelope spectrum to obtain a filtered signal; s4, calculating a fault characteristic frequency ratio of the filter signal, taking the maximum value of the fault characteristic frequency ratio as an adaptability function of the particle swarm algorithm, and updating the speed and the position of particles in the particle swarm algorithm; s5, repeating the steps S3 to S4 until the optimal position of the particle is not changed or the set iteration times are reached, and outputting the optimal filter length; s6, obtaining a signal after filtering by a filter corresponding to the optimal filter length, carrying out envelope analysis on the filtered signal, and identifying the fault type. The application can extract the fault characteristics of the rolling bearing under the conditions of strong noise and harmonic interference.
Description
Technical Field
The application relates to the technical field of rolling bearing fault detection and analysis, in particular to a rolling bearing fault vibration signal analysis method.
Background
Rolling bearings are common components in rotating machinery, and their health state is related to the reliability and stability of the operation of the equipment. The rolling bearing runs under the working conditions of high rotating speed and heavy load for a long time, faults frequently occur, and if timely maintenance cannot be achieved, other parts of the equipment are damaged, so that larger economic loss is caused. Therefore, the method has important significance for accurately identifying the faults of the rolling bearing.
In the prior art, the fault diagnosis of the rolling bearing is mainly realized by a machine learning and digital signal processing method. The common machine learning method comprises a support vector machine, a random forest, a deep belief network and the like, but the machine learning model has the problems of difficult convergence, slow training speed and difficult acquisition of fault samples. Common signal analysis methods include a spectral kurtosis method, a signal decomposition method, a blind deconvolution method and the like, but rolling bearing vibration signals can be influenced by shaft vibration, gear meshing, electromagnetic interference and external environment factors, and the energy of interference signals can be dominant, so that bearing fault characteristics are difficult to extract, and challenges are brought to monitoring of the health state of mechanical equipment.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides a rolling bearing fault vibration signal analysis method, and aims to extract the fault characteristics of a rolling bearing under the conditions of strong noise and harmonic interference.
The technical scheme adopted by the application is as follows:
a rolling bearing fault vibration signal analysis method comprises the following steps:
s1, obtaining vibration signals of a rolling bearing, and calculating characteristic frequencies corresponding to fault types of the bearing;
s2, initializing parameters of a particle swarm algorithm, and setting a range of the length of the optimizing filter and the iteration times;
s3, initializing a filter, and filtering the vibration signal by adopting blind deconvolution based on square envelope spectrum to obtain a filtered signal;
s4, calculating a fault characteristic frequency ratio of the filter signal, taking the maximum value of the fault characteristic frequency ratio as an adaptability function of a particle swarm algorithm, and updating the speed and the position of particles in the particle swarm algorithm;
s5, repeating the steps S3 to S4 until the optimal position of the particle is not changed or the set iteration times are reached, ending the iteration, and outputting the optimal filter length;
s6, obtaining a signal filtered by a filter corresponding to the optimal filter length, carrying out envelope analysis on the filtered signal, and identifying the fault type.
The further technical scheme is as follows:
the step S3 specifically comprises the following steps:
s31, initializing a filter f, setting the maximum iteration number K, and loading an original vibration signal y;
s32, initializing iteration times;
s33, calculating a filtered signal x and a square envelope spectrum S thereof;
the filtered signal calculation formula: x=y×f=af, which isIn which, represents the convolution operator, A ε R (N-L+1)×L The matrix is a Hanker matrix derived from y, N is the length of the vibration signal y, and L is the length of the filter f;
square envelope spectrum s= |f (|x|) 2 +|Hx| 2 ) I, where H is the time-domain filter of the hilbert transform of the filtered signal x, F is the (N-l+1) × (N-l+1) dimensional fourier matrix;
s34, calculating an optimal criterion M about the square envelope spectrum S p，q (s)：
Where NL is the length of s, p >0, q >0;
s35, calculating an optimal criterion M p，q (s) updating the filter f by using a gradient descent method with respect to the derivative of the filter f, and outputting a filtered signal when the number of iterations reaches a maximum number of iterations K;
in step S4, a fitness function is constructed as follows:
in the fault characteristic frequency ratioCalculated as follows:
wherein,for a frequency of +.>Amplitude of the envelope spectrum of the filtered signal at time, < >>For the characteristic frequency corresponding to the K-th type of fault, k=1, 2,..k, K represents the total number of fault types, and S (ω) is the amplitude of the envelope spectrum of the filtered signal at the frequency ω.
In the step S4, in the particle swarm optimization process, the fitness function values of the front and rear positions of the particles are compared, and the largest fitness function value is taken as a local optimal value; comparing the fitness function values of all the current particles, taking the largest fitness function value as a global optimal value, and updating the speed and the position of the particles in the particle swarm algorithm;
the speed update formula is:
the location update formula is:
in the above two formulas, O represents a particle number, o=1, 2, 3..o, O is the total number of particles; h is the iteration number;representing an inertial factor; />Representing the velocity in d-dimension in the h-th iteration of particle o; c 1 、c 2 Representing a learning factor; eta represents an interval between [0,1 ]]Random numbers of (a); />Representing the position of the individual extreme point of the particle o in the d-th dimension in h iterations; />Representing the current position of the particle o in the d-th dimension in h iterations; />Representing the position of the global extremum of the entire particle population in d-dimension in h iterations.
In step S6, a signal filtered by a filter corresponding to the optimal filter length is obtained, first, hilbert transformation is performed on the filtered signal, a mode of an analysis signal is obtained, then, fourier spectrum is obtained on the analysis signal, and a fault type is identified according to the fourier spectrum.
The beneficial effects of the application are as follows:
1. the blind deconvolution based on the square envelope spectrum is adopted to filter the original vibration signal, and the envelope spectrum of the filtered signal has good sparsity.
2. The particle swarm optimization is adopted to carry out optimization on the filter length of the blind deconvolution process, and the optimization parameters are simple and quick; the maximum value of the fault characteristic frequency ratio representing the proportion of the fault characteristic frequency in the envelope spectrum is used as the fitness function, the sparsity of the square envelope and the fault information are considered, the fault characteristic is accurately grasped, the interference of noise and harmonic waves to the characteristic frequency is filtered, and the fault information after the filtering processing is effectively reflected.
Drawings
FIG. 1 is a schematic flow chart of the present application.
Fig. 2 is a time domain, frequency spectrum and envelope spectrum of an extracted original vibration signal according to an embodiment of the present application.
Fig. 3 shows time domain and envelope spectra of a filtered signal according to an embodiment of the present application.
Detailed Description
The following describes specific embodiments of the present application with reference to the drawings.
Referring to fig. 1, the method for analyzing the fault vibration signal of the rolling bearing according to the present application comprises the following steps:
s1, obtaining vibration signals of a rolling bearing, calculating characteristic frequencies corresponding to various fault types of the bearing, and calculating the characteristic frequencies of the inner ring faults, the outer ring faults, the rolling body faults and the retainer faults of various fault types of the bearing by using the following calculation formulas:
inner ring failure:
outer ring failure:
rolling element failure:
cage failure:
wherein z represents the number of rolling elements, f represents the rotation frequency, D represents the diameter of the rolling elements, D represents the pitch diameter, and α is the contact angle.
S2, initializing parameters of a particle swarm algorithm, and setting a range of the length of the optimizing filter;
specifically, the length range of the particle swarm optimization filter is an integer between 1 and N/2, and N is the length of the vibration signal. The initial position of the particles is a random integer between 1 and N/2, and the initial speed is also a random integer; the parameter setting also includes a maximum iteration number P, and a current iteration number i.
S3, initializing a filter, filtering the vibration signal by adopting blind deconvolution based on square envelope spectrum to obtain a filtered signal, wherein the method specifically comprises the following steps of:
s31, initializing a filter f, selecting the maximum iteration number K, and loading an original vibration signal y;
s32, initializing the iteration number i' =1;
s33, calculating a filtered signal x and a square envelope spectrum S thereof;
the filtered signal calculation formula: x=y×f=af, where x represents the convolution operator, a e R (N-L+1)×L The matrix is a Hanker matrix derived from y, N is the length of the vibration signal y, and L is the length of the filter f;
square envelope spectrum s= |f (|x|) 2 +|Hx| 2 ) I, where H is a hilbert transformed time-domain filter, F is an (N-l+1) × (N-l+1) dimensional fourier matrix, expressed in the form:
s34, calculating an optimal criterion M about the square envelope spectrum S p，q (s)：
Where NL is the length of s, p >0, q >0;
s35, calculating an optimal criterion M p，q (s) derivative with respect to filter f:
optimization criterion M p，q (s) deriving the filter f as:
in the formula (A), the components of the compound,x t e, hilbert (x) are respectively the resolved signal, square envelope, hilbert transform, x of the filtered signal x t ＝x+ihilbert(x)、e＝|x+ihilbert(x)| 2 ＝|x| 2 +|Hx| 2 H is a time domain filter of hilbert (x); the "";
in the formula (A), the components of the compound,the formula of (2) is as follows:
in the formula (B), the amino acid sequence of the formula (B), i is an identity matrix; f (F) c And F s Respectively the real part and the imaginary part of the matrix F, P c And P s Real coefficients and imaginary coefficients of the fourier transform of e, respectively;
in the formula (B), the amino acid sequence of the formula (B),the formula of (2) is as follows:
and updating the filter f by using a gradient descent method according to the derivative result, and outputting a filtering signal when the iteration number i' reaches the set maximum iteration number K.
Wherein the blind deconvolution (Blind deconvolution technique based on squared envelope spectrum, SEBD) algorithm based on the square envelope spectrum described above counteracts the effects of the transmission path and the environment by finding a filter, minimizing the best criterion for the filtered signal. L according to signal square envelope p /L q For the rolling bearing, when the outer ring and the inner ring are in fault, a series of vibration signals with the characteristics of impact property and periodicity can be generated along with the rotation of the rolling bearing, the envelope of the vibration signals has good sparsity, and L p /L q The norm may represent a large class of sparse measures, L of the signal squared envelope p /L q The smaller the norm value, the better the sparsity of the representative square envelope, the above-mentioned optimal criterion M p，q The push-to procedure of(s) is as follows:
in the above-mentioned method, the step of,minimizing M p，q (s), square envelope spectrum s can obtain better sparsity.
The particle swarm algorithm (particle swarm optimization, PSO) is an iterative optimization algorithm, the system is initialized to a set of random solutions, and the global optimal value is found through iterative search. The application optimizes the filter length adopted by the SEBD algorithm filtering by using the PSO algorithm, and supposes that the particle o searches in a D dimensional space, and finds the optimal solution by iterative updating, namely, represents the optimized parameter (filter length) by using the particle position. In each iteration, the position vector s for the particle information o Velocity vector v o To express: s is(s) o ＝(s o，1 ，s o，2 ，...，s o，D )，v o ＝(v o，1 ，v o，2 ，...，v o，D )。
S4, calculating a fault characteristic frequency ratio of the filter signal, taking the maximum value of the fault characteristic frequency ratio as a fitness function of a particle swarm algorithm, and updating the speed and the position of particles in the particle swarm algorithm, wherein the method specifically comprises the following steps:
fault characteristic frequency ratioCalculated as follows:
wherein,for a frequency of +.>Amplitude of the envelope spectrum of the filtered signal at time, < >>For the characteristic frequency corresponding to a K-th type of fault, k=1, 2,..>Calculation from the formula in step S1 described above; s (omega) is the amplitude of the envelope spectrum of the filtered signal at a frequency omega;
the fitness function is constructed as follows:
after the original vibration signal is processed by the SEBD, misjudgment of the characteristic frequency may occur due to the influence of noise and harmonic waves. Therefore, an index reflecting the degree of correlation of the fault is required as an fitness function for optimizing the filter length by the particle swarm algorithm. Statistical parameters such as kurtosis, correlation coefficient and entropy are parameters commonly used in fault signal evaluation, but they are susceptible to noise interference. In the embodiment, the maximum value of the fault characteristic frequency ratio representing the proportion of the fault characteristic frequency in the envelope spectrum is taken as the fitness function CFR, the advantages of SEBD and the characteristics of CFR are based, meanwhile, the sparsity of square envelopes and fault information are considered, the fault characteristics are accurately grasped, the interference of noise and harmonic waves on the characteristic frequency is filtered, and the fault information after filtering processing is effectively reflected.
In the particle swarm optimization process, comparing fitness function values of front and rear positions of particles, and taking the largest fitness function value as a local optimal value; comparing the fitness function values of all the current particles, taking the largest fitness function value as a global optimal value, and updating the speed and the position of the particles in the particle swarm algorithm;
the speed update formula is:
the location update formula is:
in the above two formulas, O represents a particle number, o=1, 2, 3..o, O is the total number of particles; h is the iteration number;representing an inertial factor; />Representing the velocity in d-dimension in the h-th iteration of particle o; c 1 、c 2 Representing a learning factor; eta represents an interval between [0,1 ]]Random numbers of (a); />Representing the position of the individual extreme point of the particle o in the d-th dimension in h iterations; />Representing the current position of the particle o in the d-th dimension in h iterations; />Representing the position of the global extremum of the entire particle population in d-dimension in h iterations.
S5, repeating the steps S3 to S4 until the optimal position of the particle is not changed, or finishing iteration when the maximum iteration times P are reached, and outputting the optimal filter length.
Specifically, if the optimal position of the continuous 3 times of particles is not changed, the particles are considered to reach the optimal position, and the iteration can be finished in advance when the maximum iteration number P is reached, so that the calculation time is saved.
S6, obtaining a signal filtered by a filter corresponding to the optimal filter length, carrying out envelope analysis on the filtered signal, and identifying the fault type.
Specifically, a signal filtered by a filter corresponding to the optimal filter length is obtained, hilbert transformation is firstly carried out on the filtered signal, a mode of an analysis signal is obtained, then a Fourier spectrum is obtained on the analysis signal, and a fault type is identified according to the Fourier spectrum.
The technical scheme of the application is further described below with reference to specific embodiments.
The method for analyzing the fault vibration signal of the rolling bearing comprises the following steps:
s1, obtaining vibration signals of a rolling bearing of a wind turbine generator:
the overt dataset is a 6203 bearing dataset, including both artificially induced and real lesions. And a piezoelectric accelerometer is adopted to collect vibration signals of the bearing seat, and the sampling frequency is 64kHz. The data set consists of four operating conditions of varying rotational speed of the drive system, testing radial forces on the bearings, load torque on the drive system, and fault type.
And selecting a data bearing code KA08, wherein the rotating speed is 1500r/min, the radial force is 1000N, the load torque is 0.1Nm, and the bearing fault is an outer ring rotating hole fault.
The number of rolling bodies of the 6203 bearing is 8; the diameter of the rolling bodies is 6.75mm; the pitch diameter is 29.05mm; the contact angle was 0.
The failure characteristic frequency is calculated to be about f according to a failure calculation formula of the outer ring of the bearing c =76.76 Hz. Since the high frequency part of the data mainly contains noise components, and too high a sampling frequency will result in huge data volume and large calculation time. Therefore, the original data is resampled, the sampling frequency after resampling is 12800Hz, 8192 sampling points are selected as the analysis data of the experiment, and the resampled signal is shown in FIG. 2. Fig. 2a, 2b, 2c are respectively a time domain diagram, a spectrogram, an envelope spectrum of the vibration signal. It can be seen from the graph that the transient impact cannot be directly identified from fig. 2a, fig. 2b shows that the spectrum of the vibration signal is relatively blurred, and the envelope spectrum of fig. 2c has a somewhat convex peak in the low frequency part, but is relatively aliased, so that the fault characteristic frequency cannot be effectively identified.
According to characteristic frequencyThe formula calculates various fault types of the bearing: the characteristic frequencies of the inner ring faults, the outer ring faults, the rolling body faults and the retainer faults are respectively as follows:
s2, initializing parameters of a particle swarm algorithm: learning factor c 1 ＝c 2 =2.05; population size o=20; maximum number of iterations p=200; inertia factor
The particle swarm optimization filter length range is an integer between 1 and N/2, and N is the signal length N=8192. The initial position of the particles is a random integer between 1 and N/2.
S3, initializing a filter, filtering the vibration signal by adopting blind deconvolution based on square envelope spectrum, and performing iterative calculation to obtain a filtered signal;
s4, calculating a fault characteristic frequency ratio of the filter signal, taking the maximum value of the fault characteristic frequency ratio as an adaptability function of the particle swarm algorithm, and updating the speed and the position of particles in the particle swarm algorithm;
s5, repeating the steps S3 and S4 until the optimal position of the particle is not changed or the maximum iteration number P is reached, ending the iteration, and outputting the optimal filter length.
After the particle swarm algorithm of this embodiment is iterated 8 times, the global optimal position for three consecutive times is 795, so that the optimal filter length L is output best ＝795。
S6, obtaining the optimal filter length L best The corresponding filter filters the signal, and the envelope analysis is performed on the filtered signal, and the result is shown in fig. 3. FIG. 3a shows a time domain waveform of the processed signal, a clear transient impulse signal can be seen, and the envelope spectrum of FIG. 3b clearly shows the characteristic frequency of the transient impulse signal, which is the same as the failure frequency f of the signal c The frequency of the bearing fault characteristics extracted by the analysis method can be proved to be effective in identifying the fault type by the coincidence of 76.76Hz.
Claims (4)
1. The method for analyzing the fault vibration signal of the rolling bearing is characterized by comprising the following steps of:
s1, obtaining vibration signals of a rolling bearing, and calculating characteristic frequencies corresponding to fault types of the bearing;
s2, initializing parameters of a particle swarm algorithm, and setting a range of the length of the optimizing filter and the iteration times;
s3, initializing a filter, and filtering the vibration signal by adopting blind deconvolution based on square envelope spectrum to obtain a filtered signal;
s4, calculating a fault characteristic frequency ratio of the filter signal, taking the maximum value of the fault characteristic frequency ratio as an adaptability function of a particle swarm algorithm, and updating the speed and the position of particles in the particle swarm algorithm;
s5, repeating the steps S3 to S4 until the optimal position of the particle is not changed or the set iteration times are reached, ending the iteration, and outputting the optimal filter length;
s6, obtaining a signal filtered by a filter corresponding to the optimal filter length, carrying out envelope analysis on the filtered signal, and identifying the fault type;
the step S3 specifically comprises the following steps:
s31, initializing a filter f, setting the maximum iteration number K, and loading an original vibration signal y;
s32, initializing iteration times;
s33, calculating a filtered signal x and a square envelope spectrum S thereof;
the filtered signal calculation formula: x=y×f=af, where x represents the convolution operator, a e R (N-L+1)×L The matrix is a Hanker matrix derived from y, N is the length of the vibration signal y, and L is the length of the filter f;
square envelope spectrum s= |f (|x|) 2 +|Hx| 2 ) I, where H is the time-domain filter of the hilbert transform of the filtered signal x, F is the (N-l+1) × (N-l+1) dimensional fourier matrix;
s34, calculating an optimal criterion M about the square envelope spectrum S p,q (s)：
Where NL is the length of s, p >0, q >0;
s35, calculating an optimal criterion M p,q (s) updating the filter f by using a gradient descent method with respect to the derivative of the filter f, and outputting a filtered signal when the number of iterations reaches the maximum number of iterations K.
2. The method for analyzing a fault vibration signal of a rolling bearing according to claim 1, wherein in step S4, a fitness function is constructed as follows:
CFR＝max(FCFR(f c k ))
in the formula, the failure characteristic frequency ratio FCFR (f c k ) Calculated as follows:
wherein S (i.f c k ) Is of frequency i.f c k Amplitude, f of envelope spectrum of the filtered signal c k For the characteristic frequency corresponding to the K-th type of faults, k=1, 2, …, K represents the total number of fault types, and S (ω) is the amplitude of the envelope spectrum of the filtered signal when the frequency is ω.
3. The method according to claim 1, wherein in step S4, in the particle swarm optimization, fitness function values of front and rear positions of particles are compared, and a maximum fitness function value is used as a local optimal value; comparing the fitness function values of all the current particles, taking the largest fitness function value as a global optimal value, and updating the speed and the position of the particles in the particle swarm algorithm;
the speed update formula is:
the location update formula is:
in the above two formulas, O represents a particle number, o=1, 2,3 …, O is the total number of particles; h is the iteration number;representing an inertial factor; />Representing the velocity in d-dimension in the h-th iteration of particle o; c 1 、c 2 Representing a learning factor; eta represents an interval between [0,1 ]]Random numbers of (a); />Representing the position of the individual extreme point of the particle o in the d-th dimension in h iterations; />Representing the current position of the particle o in the d-th dimension in h iterations; />Representing the position of the global extremum of the entire particle population in d-dimension in h iterations.
4. The method according to claim 1, wherein in step S6, a filtered signal of a filter corresponding to an optimal filter length is obtained, a hilbert transform is first performed on the filtered signal, a modulus of an analysis signal is obtained, then a fourier spectrum is obtained on the analysis signal, and a fault type is identified based on the fourier spectrum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111627847.9A CN114441172B (en) | 2021-12-28 | 2021-12-28 | Rolling bearing fault vibration signal analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111627847.9A CN114441172B (en) | 2021-12-28 | 2021-12-28 | Rolling bearing fault vibration signal analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114441172A CN114441172A (en) | 2022-05-06 |
| CN114441172B true CN114441172B (en) | 2023-12-15 |
Family
ID=81366553
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111627847.9A Active CN114441172B (en) | 2021-12-28 | 2021-12-28 | Rolling bearing fault vibration signal analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114441172B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115859091B (en) * | 2022-11-01 | 2023-05-26 | 哈尔滨工业大学 | Bearing fault feature extraction method, electronic equipment and storage medium |
| CN117932302B (en) * | 2024-01-22 | 2024-07-12 | 浙江大学 | Fault signal extraction method, device, equipment and medium for mechanical device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107356432A (en) * | 2017-07-12 | 2017-11-17 | 石家庄铁道大学 | Fault Diagnosis of Roller Bearings based on frequency domain window experience small echo resonance and demodulation |
| CN108426715A (en) * | 2018-06-13 | 2018-08-21 | 福州大学 | Rolling bearing Weak fault diagnostic method based on PSO-VMD-MCKD |
| CN111881736A (en) * | 2020-06-18 | 2020-11-03 | 东南大学 | Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition |
| CN112347854A (en) * | 2020-10-12 | 2021-02-09 | 西安电子科技大学 | Rolling bearing fault diagnosis method and system, storage medium, equipment and application |
-
2021
- 2021-12-28 CN CN202111627847.9A patent/CN114441172B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107356432A (en) * | 2017-07-12 | 2017-11-17 | 石家庄铁道大学 | Fault Diagnosis of Roller Bearings based on frequency domain window experience small echo resonance and demodulation |
| CN108426715A (en) * | 2018-06-13 | 2018-08-21 | 福州大学 | Rolling bearing Weak fault diagnostic method based on PSO-VMD-MCKD |
| CN111881736A (en) * | 2020-06-18 | 2020-11-03 | 东南大学 | Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition |
| CN112347854A (en) * | 2020-10-12 | 2021-02-09 | 西安电子科技大学 | Rolling bearing fault diagnosis method and system, storage medium, equipment and application |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114441172A (en) | 2022-05-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114441172B (en) | Rolling bearing fault vibration signal analysis method | |
| CN109682601B (en) | Early fault identification method for rolling bearing under variable rotating speed working condition | |
| CN112052796A (en) | Permanent magnet synchronous motor fault diagnosis method based on deep learning | |
| CN107727399B (en) | Fault Diagnosis of Roller Bearings based on dual-tree complex wavelet packet stream shape domain noise reduction | |
| CN106487359B (en) | The building method of Morphologic filters based on self-adapting multi-dimension AVG-Hat transformation | |
| CN109813547B (en) | Rotary machine local fault diagnosis method based on sparse decomposition optimization algorithm | |
| CN108760310B (en) | Stochastic resonance rolling bearing fault diagnosis method based on novel signal-to-noise ratio index | |
| Ding et al. | Sparsity-based algorithm for condition assessment of rotating machinery using internal encoder data | |
| CN109655266B (en) | Wind turbine generator bearing fault diagnosis method based on AVMD and spectrum correlation analysis | |
| Zhang et al. | Rolling bearing fault convolutional neural network diagnosis method based on casing signal | |
| CN110940522A (en) | Bearing fault periodic pulse sparse separation and diagnosis method under strong background noise | |
| CN111076934A (en) | Method for diagnosing potential fault of bearing based on S transformation | |
| CN112362343A (en) | Distributed fault feature extraction method for gearbox under variable rotating speed based on frequency modulation dictionary | |
| CN115809399A (en) | Self-adaptive signal noise reduction decomposition method for composite fault recognition of mechanical transmission system | |
| CN106500991B (en) | Bearing fault signal characteristic extracting methods based on self-adapting multi-dimension AVG-Hat transformation | |
| CN111610024A (en) | Gear crack fault diagnosis method based on vibration signals | |
| CN105352726A (en) | Fault diagnosis method for gear | |
| CN114813123A (en) | Rolling bearing weak fault diagnosis method based on PSO-VMD-MCKD | |
| CN110543820A (en) | Fault rolling bearing modal parameter extraction method based on edited cepstrum | |
| CN117370781A (en) | RV reducer composite defect feature extraction method and device | |
| CN117309377A (en) | Gear box variable rotation speed compound fault diagnosis method | |
| CN111323233B (en) | Local mean decomposition method for low-speed rotating machine fault diagnosis | |
| CN117109923A (en) | Rolling bearing fault diagnosis method and system | |
| CN114912480A (en) | Fault diagnosis method and system based on depth residual variation self-encoder | |
| CN112747925B (en) | Rolling bearing fault diagnosis method based on composite morphological filtering |
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 |