CN114061947B - Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox - Google Patents
Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox Download PDFInfo
- Publication number
- CN114061947B CN114061947B CN202111158249.1A CN202111158249A CN114061947B CN 114061947 B CN114061947 B CN 114061947B CN 202111158249 A CN202111158249 A CN 202111158249A CN 114061947 B CN114061947 B CN 114061947B
- Authority
- CN
- China
- Prior art keywords
- frequency
- fault
- sparse time
- gearbox
- frequency analysis
- 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
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
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/028—Acoustic or vibration analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H13/00—Measuring resonant frequency
-
- 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
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
Abstract
The invention discloses a method and a system for diagnosing variable rotating speed faults of a gearbox based on sparse time-frequency analysis, wherein the method comprises the following steps of S100: receiving a vibration signal; s200: when a bearing of the gearbox is detected to be in fault, the resonance frequency is taken as the center frequency, and band-pass filtering is carried out on the vibration signal so as to eliminate the interference of gear meshing components; when a local fault of the gear is detected, eliminating the interference of gear meshing components by adopting a morphological component separation method to obtain a gear fault impact signal; s300: carrying out envelope demodulation processing on the obtained fault impact signal to obtain an envelope signal; s400: constructing an over-complete dictionary, and obtaining a sparse time-frequency distribution map by using a sparse time-frequency analysis method; s500: and judging whether the gearbox has faults and the type of the faults according to whether the sparse time-frequency distribution map contains the preset fault characteristic frequency. The invention can realize fault diagnosis without prior knowledge of key phase signals and fault characteristic frequency components, and has the advantages of high calculation precision, good noise suppression effect and the like.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a gearbox variable-rotation-speed fault diagnosis method and system based on sparse time-frequency analysis.
Background
The key parts such as bearings and gears are widely applied to the fields of engineering machinery, wind driven generators and the like, and whether the running state of the key parts is normal or not is related to whether the whole equipment can run reliably and stably or not. Therefore, the health state monitoring research is carried out on key parts such as bearings and gears, and the method has important significance for guaranteeing safe and stable operation of mechanical equipment. Mechanical vibration is easy to measure, and the vibration response of the equipment contains rich health state information of the equipment, so that the monitoring and fault diagnosis of the equipment state obtained by analyzing the vibration signal are widely concerned.
The gear box is a key part of mechanical power transmission, and under the working condition of high rotating speed fluctuation, the gears generate vibration response with obvious non-stationarity in the meshing process. When considering the influence of the transmission path, the modulation characteristics of the signal are more complicated, for example, the frequency of the fault characteristic caused by the fault also changes with time, and the frequency modulation characteristic appears. For frequency characteristics changing along with time, spectral analysis methods such as Fourier transform and the like suitable for constant rotating speed working conditions cannot meet the requirements of signal processing. The common means adopts an order tracking technology, and develops the fault diagnosis technical research of the variable-speed gearbox by extracting the vibration response characteristics and the dynamic characteristics. However, the effectiveness of the order tracking method is affected by factors such as the accuracy of the measured rotational speed, interpolation errors, and transmission paths. Therefore, time-frequency analysis methods that do not require resampling have gained wide attention. However, the existing time-frequency analysis method is limited by the heisenberg inaccurate measurement principle (such as linear transformation like short-time fourier transformation and wavelet transformation), or is interfered by cross terms (such as bilinear transformation like Wigner-Ville distribution), or is greatly interfered by noise (such as synchronous compression transformation), or still outputs curves which can cause misdiagnosis when the equipment is in a healthy state (such as a ridge line extraction method).
Disclosure of Invention
In order to solve the above problems, embodiments of the present application provide a method and a system for diagnosing a variable rotation speed fault of a gearbox based on sparse time-frequency analysis.
In a first aspect, an embodiment of the present application provides a gearbox variable-speed fault diagnosis method based on sparse time-frequency analysis, including the following steps:
s100: receiving a vibration signal of the gearbox acquired by an acceleration sensor under a variable rotating speed working condition;
s200: when a bearing in a gear box is detected to be in fault, the resonance frequency of the gear box or the acceleration sensor is taken as the center frequency, and based on the selected bandwidth, band-pass filtering is carried out on the received vibration signal so as to eliminate the interference of gear meshing components; when a local fault of the gear is detected, eliminating the interference of gear meshing components by adopting a morphological component separation method to obtain a gear fault impact signal;
s300: carrying out Hilbert transform-based envelope demodulation processing on the obtained fault impact signal to obtain an envelope signal;
s400: constructing an over-complete dictionary based on short-time Fourier transform, and performing sparse time-frequency analysis by using an iterative soft threshold algorithm to obtain a sparse time-frequency distribution map of the envelope signal;
s500: and judging whether the gearbox generates faults and the type of the generated faults according to whether the sparse time-frequency distribution map contains preset fault characteristic frequency.
Further preferably, in the step S400, the expression adopted for constructing the over-complete dictionary based on the short-time fourier transform is as follows:
A＝[W 1 B … W i B … W K B]……(1)；
in the formula (1), the reaction mixture is,
for intercepting the i-th segment of the time domain signal, w = [ w [0 ]] w[1] … w[N-1]]For a window function satisfying a strong overlap-add condition, B ∈ C L×L Is an inverse fourier transform matrix.
Further preferably, in the step 400, the following objective function formula is adopted in the method for sparse time-frequency analysis by using the iterative soft threshold algorithm:
in the formula (2), y is an envelope signal in the step 300, the optimization-minimization method is used for solving the objective function formula (2), a short-time Fourier transform operator is used for improving the operation speed, meanwhile, the memory of a computer is saved, an iterative step expression based on an iterative soft threshold contraction algorithm is obtained, and the iterative step expression of the algorithm is as follows:
in formula (3), the optimized x is plotted using a color scale j+1 And obtaining a sparse time-frequency representation result.
Further preferably, the nearest neighbor function expression constructed in the optimization-minimization method for solving the objective function expression is:
wherein, in order to ensure that all x in the formula (4),
is non-negative, matrix α I-A H A adopts a semi-positive definite matrix, so that the parameter alpha needs to satisfy the expression:
α≥max{eig(A H A)}……(5)
in the formula (5), eig (·) represents a eigenvalue operator.
Further preferably, in formula (4), G j (x) And expanding to obtain an expression:
in the formula (6), the reaction mixture is,
and C is independent of x.
Further preferably, the solution of the vector x is expressed by a soft threshold function, and the expression is as follows:
in the formula (7), soft: C N ×R + → C is an operator for element-by-element computation, defined by the following expression:
in the formula (8), z i Is the ith element of the vector z;
the solution to the objective function is found as the following expression:
wherein alpha is not less thanmax{eig(A H A)}。
In a second aspect, the application provides a gearbox variable-speed fault diagnosis system based on sparse time-frequency analysis, and the method of the first aspect is adopted, and comprises the following steps:
the signal receiving module is configured to receive vibration signals of the gearbox acquired by the acceleration sensor under the variable-speed working condition;
the impact characteristic extraction module is configured to detect that a bearing in a gear box has a fault, and perform band-pass filtering on a received vibration signal based on a selected bandwidth by taking the resonance frequency of the gear box or the acceleration sensor as a central frequency so as to eliminate interference of gear meshing components; when a local fault of the gear is detected, eliminating the interference of gear meshing components by adopting a morphological component separation method to obtain a gear fault impact signal;
the envelope demodulation module is configured to perform envelope demodulation processing based on Hilbert transform on the obtained fault impact signal to obtain an envelope signal;
the sparse time-frequency analysis module is configured to construct an over-complete dictionary based on short-time Fourier transform, and sparse time-frequency analysis is carried out by using an iterative soft threshold algorithm to obtain a sparse time-frequency distribution map of the envelope signal;
and the fault judging module is configured to judge whether the gearbox generates faults and the type of the generated faults according to whether the sparse time-frequency distribution map contains preset fault characteristic frequency.
In a third aspect, the present application provides an electronic device, comprising:
a memory for storing executable program code; and
a processor, connected to the memory, for executing a computer program corresponding to the executable program code by reading the executable program code to perform the steps as in the first aspect.
In a fourth aspect, the present application provides a storage medium storing executable program code, and at least one processor reads the executable program code to execute a computer program corresponding to the executable program code, so as to perform at least one step of the first aspect.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
the method comprises the steps of 1, utilizing a resonance demodulation technology, carrying out band-pass filtering on a vibration signal in a resonance frequency band to avoid interference of gear meshing, utilizing sparsity of fault characteristic frequency in an envelope signal, and calculating sparse time-frequency distribution with high resolution through an iterative soft threshold algorithm, so that accurate extraction of the fault characteristics of the gearbox bearing under the condition of variable rotating speed is realized.
The method does not need prior knowledge of key phase signals and fault characteristic frequency components, and has the advantages of high calculation precision, good noise suppression effect and the like.
Drawings
FIG. 1 is a flowchart illustrating a method for diagnosing a variable-speed fault of a gearbox based on sparse time-frequency analysis according to an embodiment of the present invention;
FIG. 2 is a labeled diagram of a test bench with a gear box according to the first embodiment;
FIG. 3 is a schematic diagram of the signal processing results of a gearbox with bearing outer ring faults in the first embodiment;
FIG. 4 is a block diagram of a gearbox variable-speed fault diagnosis system based on sparse time-frequency analysis in the second embodiment.
Detailed Description
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and specific embodiments.
Example one
The embodiment of the application provides a gearbox variable-speed fault diagnosis method based on sparse time-frequency analysis, which is applied to the variable-speed working condition of a gearbox. Further, referring to fig. 2, a test bench comprising a gear box comprises an ac motor 1, a frequency converter for controlling the rotational speed of the motor, an eddy current sensor 2 for measuring the rotational speed, a gear box 3, a magnetic particle brake 4 for adding a load, and a magnetic particle brake controller for controlling the magnitude of the load.
Further, the gear box in the present embodiment employs a primary speed reducer. The bearing comprises a plurality of normal bearings 6, a large gear with 39 teeth, a small gear with 28 teeth, an experimental bearing 5 containing outer ring faults, wherein the experimental bearing is located at a measuring point 3, the type of the bearing is 6203, and the parameters of the bearing are shown in table 1. In the experiment, the sampling frequency is 51.2kHz, the signal duration is 5.12 seconds, and the number of sampling points is 262144. In the ramp-up experiment, the frequency of rotation was gradually increased from 7.9Hz to 11.8 Hz.
TABLE 1 bearing geometry
Referring to the attached drawing 1, an embodiment of the present application provides a gearbox variable speed fault diagnosis method based on sparse time-frequency analysis, including the following steps:
step S100: and receiving vibration signals of the gearbox acquired by the acceleration sensor under the variable-speed working condition.
Step S200: when a bearing in the gear box is detected to be in fault, the resonance frequency of the gear box or the acceleration sensor is taken as the center frequency, and based on the selected bandwidth, the received vibration signal is subjected to band-pass filtering so as to eliminate the interference of gear meshing components. When the local fault of the gear is detected, the interference of the meshing components of the gear is eliminated by adopting a form component separation method, and a gear fault impact signal is obtained. Further in this step, the acquired vibration signal is band-pass filtered with the 3-fold frequency 18kHz of the structure resonance frequency 6kHz as the center frequency and the bandwidth of 2 kHz.
Step S300: and carrying out envelope demodulation processing based on Hilbert (Hilbert) transformation on the obtained fault impact signal to obtain an envelope signal.
Step S400: and constructing an over-complete dictionary based on short-time Fourier transform, and performing sparse time-frequency analysis by using an iterative soft threshold algorithm to obtain a sparse time-frequency distribution map of the envelope signal. Further, in this step, sparse time-frequency analysis is performed on the envelope of the filtered signal, and the obtained result is shown in fig. 3 (d).
Step S500: and judging whether the gearbox has faults and the type of the faults according to whether the sparse time-frequency distribution map contains preset fault characteristic frequency. As can be seen from FIG. 3 (d), the time-frequency diagram well detects the fault characteristic frequency of the bearing, and the time and frequency resolution is greatly improved, so that the effectiveness of the fault diagnosis of the bearing in the gearbox based on the resonance demodulation method and the sparse time-frequency analysis method under the variable rotating speed is verified.
Further, in the present embodiment, in order to detect the fault information of the bearing in the gear box, the fault information is separated from other interference frequency components by using a resonance demodulation method. This step of the present embodiment utilizes structural resonance to perform resonance demodulation. The whole section of long signals collected under the variable rotating speed are subjected to Fast Fourier Transform (FFT), in the obtained frequency spectrum, the frequency amplitude changing along with the rotating speed is averaged by time, and the resonance frequency is strengthened because the resonance frequency does not change along with the rotating speed. Therefore, a significant resonance frequency and its frequency doubling appear in the calculated spectrogram. The resonant frequency may be modulated by the impact caused by the bearing failure, causing the failure information to be carried at the resonant frequency. Therefore, in the embodiment, the characteristic frequency of the bearing fault can be obtained by filtering the signal by taking the resonance frequency or the frequency multiplication thereof as the central frequency and then demodulating the amplitude.
Referring to fig. 3 (c), a frequency spectrum obtained by performing Fast Fourier Transform (FFT) on the signal acquired under the ramp-up condition in the present embodiment is shown, and fig. 3 (c) is the entire signal frequency spectrum shown in fig. 3 (a). It can be seen from the figure that the resonance frequency occurs at 6kHz and that there is a double frequency in the calculated spectrogram. When the frequency is less than 10kHz, frequency components in a frequency spectrum are rich, and if 6kHz is selected as the center frequency for filtering, the obtained signal still has the possibility of being interfered by other components of the system. This embodiment therefore filters the signal with a frequency doubling of the resonance frequency greater than 10kHz as the center frequency. Because the amplitude of 18kHz is larger and the interference of other components is smaller, the experiment takes 18kHz as the center frequency, and the bandwidth is 2kHz to filter the vibration signal.
Referring to fig. 3 (b), the rotational speed information obtained by the eddy current sensor is shown. The rotating speed information obtained by the sensor is only used for verifying whether the extracted fault characteristic frequency is accurate or not, and the rotating speed information is not needed in the algorithm execution process. Fig. 3 (e) shows the result of STFT of the signal shown in fig. 3 (a). It can be seen from the figure that the fault characteristics of the bearing are difficult to detect in the time-frequency diagram because of the interference of the gear meshing components. Fig. 3 (f) shows the result of performing STFT directly on the envelope of the filtered signal. It can be seen from the figure that the time and frequency resolution is lower compared to fig. 3 (d). Through the above examples, the effectiveness and superiority of the bearing fault diagnosis method in the gearbox are verified.
To explain further, in step S400, the short-time fourier transform-based overcomplete dictionary is constructed using the following expression:
A＝[W 1 B …W i B … W K B]……(1)；
in the formula (1), the acid-base catalyst,
is used for intercepting the ith segment of time domain signal, w = [ w [0 ]] w[1] … w[N-1]]For a window function satisfying a strong overlap-add condition, B ∈ C L×L Is an inverse fourier transform matrix. In this step, when the envelope signal is processed, the number of points of the envelope signal subjected to fourier transform is used as the number of sampling points, and preferably, a base 2 fast fourier transform algorithm in short-time fourier transform is adopted, and the frequency resolution in the calculated frequency spectrum is 0.1953Hz.
In step S400, the method for performing sparse time-frequency analysis by using the iterative soft threshold algorithm adopts the following objective function formula:
in the formula (2), y is an envelope signal in the step 300, the optimization-minimization method is used for solving the objective function formula (2), a short-time Fourier transform operator is used for improving the operation speed, meanwhile, the memory of a computer is saved, an iterative step expression based on an iterative soft threshold contraction algorithm is obtained, and the iterative step expression of the algorithm is as follows:
in the formula (3), the optimized x is drawn by adopting a color scale j+1 And obtaining a sparse time-frequency representation result.
Further preferably, the neighbor function expression constructed in the solution of the objective function equation by the optimization-minimization method is:
wherein, in order to ensure that all x in the formula (4),
is non-negative, matrix α I-A H A adopts a semi-positive definite matrix, so that the parameter alpha needs to satisfy the expression:
α≥max{eig(A H A)}……(5)
in the formula (5), eig (·) represents a eigenvalue operator.
Further preferably, in formula (4), G j (x) And expanding to obtain an expression:
in the formula (6), the reaction mixture is,
and C is independent of x.
Further preferably, the solution of the vector x is expressed by a soft threshold function, and the expression is as follows:
in formula (7), soft: C N ×R + → C is an operator for element-by-element computation, which can be defined by the following expression:
in the formula (8), z i Is the ith element of the vector z;
the solution to the objective function is found as the following expression:
where α ≧ max { eig (A) H A)}。
Therefore, to use the ISTFT and STFT fast operators, the formula based on the ISTA algorithm can be further written as:
in the formula (3), the optimized x is plotted by a color scale j+1 And obtaining a sparse time-frequency representation result.
It can be seen that the equation (1) to equation (9) in this embodiment is an iterative Soft-threshold puncturing Algorithm (ISTA), and the derivation process of the ISTA is also a process of the forward-backward splitting Algorithm.
When the envelope signal is particularly long, matrices A and A H Will also be very large, so that the matrix A or A is in common with H When multiplying, the operation speed is very slow and the computer memory is very occupied. Based on the above analysis, since the multiplication Ax is a short-time inverse fourier transform (ISTFT) on x, the operation Ax can be directly replaced by an ISTFT operator, i.e., ISTFT (x); likewise, multiplication operation A H y is short-time Fourier transform (STFT) performed on y, and the STFT operator, namely STFT (y), can be directly used for replacingOperation A H y, thereby avoiding the multiplication of large matrixes, greatly increasing the operation speed and saving the memory of the computer.
Further, in the gear box variable-speed fault diagnosis method based on sparse time-frequency analysis, the resonance demodulation technology is utilized, the signals are subjected to band-pass filtering in the resonance frequency band to avoid the interference of gear meshing, and the sparse time-frequency distribution with high resolution is calculated by utilizing the sparsity of fault characteristic frequencies in envelope signals and an iterative soft threshold algorithm, so that the fault characteristics of the gear box bearing under the variable-speed condition are accurately extracted. Compared with the prior art, the method does not need prior knowledge of key phase signals and fault characteristic frequency components, and has the advantages of high calculation precision, good noise suppression effect and the like.
Example two
Referring to fig. 4, an embodiment of the present application provides a gearbox variable speed fault diagnosis system based on sparse time-frequency analysis, and a method in the first embodiment is adopted, where the method includes:
the signal receiving module 100 is configured to receive a vibration signal of the gearbox acquired by the acceleration sensor under a variable-speed working condition.
The impact characteristic extraction module 200 is configured to perform band-pass filtering on a received vibration signal based on a selected bandwidth by taking a resonance frequency of the gearbox or the acceleration sensor as a center frequency when detecting that a bearing in the gearbox is in fault, so as to eliminate interference of gear meshing components; when the local fault of the gear is detected, the interference of the meshing components of the gear is eliminated by adopting methods such as form component separation and the like, and a gear fault impact signal is obtained.
And an envelope demodulation module 300 configured to perform hubert transform-based envelope demodulation processing on the obtained fault impact signal to obtain an envelope signal.
The sparse time-frequency analysis module 400 is configured to construct an overcomplete dictionary based on short-time fourier transform, and obtain a sparse time-frequency distribution map of the envelope signal by using a sparse time-frequency analysis method.
The fault judging module 500 is configured to judge whether the gearbox generates a fault and the type of the generated fault according to whether the sparse time-frequency distribution map contains a preset fault characteristic frequency.
EXAMPLE III
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein, as described below.
The embodiment provides an electronic device, including: a memory for storing executable program code; and a processor, connected to the memory, for executing a computer program corresponding to the executable program code by reading the executable program code to perform the steps of the sparse time-frequency analysis-based variable-speed fault diagnosis method for the gearbox according to the first embodiment.
The present embodiment provides a storage medium, which stores executable program codes, and at least one processor reads the executable program codes to run a computer program corresponding to the executable program codes, so as to perform at least one step of the sparse time-frequency analysis-based method for diagnosing a variable rotational speed fault of a gearbox according to the first embodiment.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A gearbox variable-rotation-speed fault diagnosis method based on sparse time-frequency analysis is characterized by comprising the following steps of:
s100: receiving a vibration signal of the gearbox acquired by an acceleration sensor under a variable rotating speed working condition;
s200: when a bearing in a gear box is detected to be in fault, taking the resonance frequency of the gear box or the acceleration sensor as a central frequency, and carrying out band-pass filtering on a received vibration signal based on a selected bandwidth so as to eliminate the interference of gear meshing components; when a local fault of the gear is detected, eliminating the interference of gear meshing components by adopting a morphological component separation method to obtain a gear fault impact signal;
s300: carrying out envelope demodulation processing based on Hilbert transform on the obtained fault impact signal to obtain an envelope signal;
s400: constructing an over-complete dictionary based on short-time Fourier transform, and performing sparse time-frequency analysis on the demodulated envelope signal by using an iterative soft threshold algorithm to obtain a sparse time-frequency distribution map of the envelope signal;
the method for sparse time-frequency analysis by using the iterative soft threshold algorithm adopts the following objective function formula:
in the formula (2), y is an envelope signal in the step 300, the optimization-minimization method is used for solving the objective function formula (2), a short-time Fourier transform operator is used for improving the operation speed, meanwhile, the memory of a computer is saved, and an algorithm iteration step expression of the sparse time-frequency analysis method based on the iteration soft threshold shrinkage algorithm is obtained and is as follows:
in the formula (3), the optimized x is drawn by adopting a color scale j+1 Obtaining a sparse time frequency representation result; s500: and judging whether the gearbox generates faults and the type of the generated faults according to whether the sparse time-frequency distribution map contains preset fault characteristic frequency.
2. The gearbox variable-speed fault diagnosis method based on sparse time-frequency analysis according to claim 1, wherein in the step S400, the over-complete dictionary based on short-time fourier transform is constructed by using the following expression:
A＝[W 1 B…W i B…W K B]…… (1)；
in the formula (1), the acid-base catalyst,
is used for intercepting the ith segment of time domain signal, w = [ w [0 ]] w[1]…w[N-1]]For a window function satisfying a strong overlap-add condition, B ∈ C L×L Is an inverse fourier transform matrix.
3. The sparse time-frequency analysis-based gearbox variable-rotation-speed fault diagnosis method as claimed in claim 1, wherein a neighbor function expression constructed in the optimization-minimization method for solving the objective function expression is as follows:
wherein, in order to ensure that all x in the formula (4),
is non-negative, matrix α I-A H A adopts a semi-positive definite matrix, so that the parameter alpha needs to satisfy the expression:
α≥max{eig(A H A)}…… (5)
in the formula (5), eig (·) represents a eigenvalue operator.
4. The gearbox variable-speed fault diagnosis method based on sparse time-frequency analysis as claimed in claim 3, wherein in formula (4), G j (x) And expanding to obtain an expression:
in the formula (6), the reaction mixture is,
and C is independent of x.
5. The sparse time-frequency analysis-based gearbox variable-speed fault diagnosis method of claim 4, wherein a solution of the vector x is represented by a soft threshold function, and an expression is as follows:
in formula (7), soft: C N ×R + → C is an operator for element-by-element computation, defined as the following expression:
in the formula (8), z i Is the ith element of vector z;
the solution to the objective function is found as the following expression:
where α ≧ max { eig (A) H A)}。
6. A gearbox variable-rotation-speed fault diagnosis system based on sparse time-frequency analysis adopts the method of any one of claims 1 to 5, and is characterized by comprising the following steps:
the signal receiving module is configured to receive a vibration signal of the gearbox under a variable rotating speed working condition, wherein the vibration signal is acquired by the acceleration sensor;
the impact characteristic extraction module is configured to detect that when a bearing in a gear box breaks down, the resonance frequency of the gear box or the acceleration sensor is taken as the center frequency, and based on the selected bandwidth, the received vibration signal is subjected to band-pass filtering to eliminate the interference of gear meshing components; when a local fault of the gear is detected, eliminating the interference of gear meshing components by adopting a morphological component separation method to obtain a gear fault impact signal;
the envelope demodulation module is configured to perform envelope demodulation processing based on Hilbert transform on the obtained fault impact signal to obtain an envelope signal;
the sparse time-frequency analysis module is configured to construct an over-complete dictionary based on short-time Fourier transform, and sparse time-frequency analysis is carried out by utilizing an iterative soft threshold algorithm to obtain a sparse time-frequency distribution map of the envelope signal; the method for sparse time-frequency analysis by using the iterative soft threshold algorithm adopts the following objective function formula:
in the formula (2), y is an envelope signal in the step 300, the optimization-minimization method is used for solving the objective function formula (2), a short-time Fourier transform operator is used for improving the operation speed, meanwhile, the memory of a computer is saved, and an iterative step expression of the sparse time-frequency analysis method based on the iterative soft threshold shrinkage algorithm is obtained as follows:
in formula (3), the optimized x is plotted using a color scale j+1 Obtaining a sparse time frequency representation result;
and the fault judging module is configured to judge whether the gearbox generates faults and the type of the generated faults according to whether the sparse time-frequency distribution map contains preset fault characteristic frequency.
7. An electronic device, comprising:
a memory for storing executable program code; and
a processor connected to the memory, for executing a computer program corresponding to the executable program code by reading the executable program code, so as to execute the steps of the sparse time-frequency analysis-based gearbox variable speed fault diagnosis method according to any one of claims 1 to 5.
8. A storage medium storing executable program code, wherein at least one processor reads the executable program code to run a computer program corresponding to the executable program code to perform at least one step of the sparse time-frequency analysis based gearbox variable speed fault diagnosis method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111158249.1A CN114061947B (en) | 2021-09-29 | 2021-09-29 | Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111158249.1A CN114061947B (en) | 2021-09-29 | 2021-09-29 | Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114061947A CN114061947A (en) | 2022-02-18 |
| CN114061947B true CN114061947B (en) | 2023-01-24 |
Family
ID=80233964
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111158249.1A Active CN114061947B (en) | 2021-09-29 | 2021-09-29 | Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114061947B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115524150B (en) * | 2022-09-13 | 2024-04-05 | 西安交通大学 | Rotary machine fault detection method based on sparse time domain synchronous average |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| NO332138B2 (en) * | 2009-11-09 | 2016-04-11 | Rolls Royce Marine As Power Electric Systems Bergen | Hybrid propulsion system for a vessel |
| CN105928702B (en) * | 2016-04-29 | 2017-03-08 | 石家庄铁道大学 | Variable working condition box bearing method for diagnosing faults based on form PCA |
| CN108152033B (en) * | 2017-12-14 | 2019-07-23 | 东华大学 | A kind of compound Weak fault diagnostic method of the gear-box of sparse disjunctive model |
| CN108507789B (en) * | 2018-05-31 | 2020-02-25 | 燕山大学 | Rolling bearing fault sparse diagnosis method |
| CN109190464B (en) * | 2018-07-24 | 2021-09-17 | 南京航空航天大学 | Mechanical fault intelligent diagnosis method based on transfer learning under variable working conditions |
| CN111626312A (en) * | 2019-02-28 | 2020-09-04 | 武汉科技大学 | Wind turbine bearing fault diagnosis method based on fast multi-separation dictionary learning |
| CN110516724B (en) * | 2019-06-04 | 2022-10-25 | 沈阳瑞初科技有限公司 | High-performance multi-layer dictionary learning characteristic image processing method for visual battle scene |
| CN111089720B (en) * | 2020-01-16 | 2021-10-08 | 山东科技大学 | Regularization sparse filtering method suitable for gear fault diagnosis under variable rotating speed |
| CN113295410B (en) * | 2021-05-14 | 2022-09-02 | 上海交通大学 | Bearing fault diagnosis method under variable rotating speed working condition |
-
2021
- 2021-09-29 CN CN202111158249.1A patent/CN114061947B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114061947A (en) | 2022-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings | |
| Saidi et al. | Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis | |
| Van Hecke et al. | Low speed bearing fault diagnosis using acoustic emission sensors | |
| Yan et al. | Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis | |
| Yang et al. | ARX model-based gearbox fault detection and localization under varying load conditions | |
| Feng et al. | A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines | |
| CN112101245B (en) | Short-time Fourier transform mechanical impact feature extraction method based on frequency domain window function | |
| Cong et al. | Research of singular value decomposition based on slip matrix for rolling bearing fault diagnosis | |
| Chaabi et al. | Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses | |
| CN109765055A (en) | Rolling bearing fault testing method and system based on EWT, spectrum virtual value and KNN | |
| Li et al. | Detection of bearing faults using a novel adaptive morphological update lifting wavelet | |
| Van Hecke et al. | On the use of spectral averaging of acoustic emission signals for bearing fault diagnostics | |
| CN112668518A (en) | VMSST time-frequency analysis method for vibration fault signal | |
| Kumar et al. | A combined approach for weak fault signature extraction of rolling element bearing using Hilbert envelop and zero frequency resonator | |
| Bai et al. | Adaptive order tracking technique using recursive least-square algorithm | |
| CN114061947B (en) | Sparse time-frequency analysis-based variable-rotation-speed fault diagnosis method and system for gearbox | |
| Zhang et al. | Improved local cepstrum and its applications for gearbox and rolling bearing fault detection | |
| Berntsen et al. | Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics | |
| Ahsan et al. | Early-stage faults detection using harmony search algorithm and stft-based spectral kurtosis | |
| Pang et al. | The evolved kurtogram: a novel repetitive transients extraction method for bearing fault diagnosis | |
| Huang et al. | Short-time kurtogram for bearing fault feature extraction under time-varying speed conditions | |
| CN110866519B (en) | Rolling bearing fault diagnosis method based on Fourier decomposition and multiscale permutation entropy deviation value | |
| Kim et al. | Effective band-selection algorithm for rolling element bearing diagnosis using AE sensor data under noisy conditions | |
| Jin et al. | Informative frequency band selection based on a new indicator: Accuracy rate | |
| Ai et al. | Application of order cepstrum and neural network to gear fault detection |
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 |