CN114136604A - Rotary equipment fault diagnosis method and system based on improved sparse dictionary - Google Patents
Rotary equipment fault diagnosis method and system based on improved sparse dictionary Download PDFInfo
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Abstract
The invention provides a fault diagnosis method and a fault diagnosis system for rotary equipment based on an improved sparse dictionary, and relates to the technical field of nuclear power safety management, wherein the method comprises the following steps: s1: acquiring a vibration signal, and performing k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2k power of equal frequency bands; s2: constructing an over-complete atom library to obtain sparse coefficients in the over-complete atom library, obtain reconstruction sparse expression, and performing single-branch reconstruction on each component signal; s3: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity; s4: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault. According to the method, the sparse dictionary method is improved to extract the fault signals, the key characteristic signals are reserved, different fault models are established according to the fault types, and finally fault diagnosis is realized according to the classification method, so that the equipment fault diagnosis accuracy is effectively improved.
Description
Technical Field
The invention relates to the technical field of nuclear power safety management,
in particular, the invention relates to a fault diagnosis method and system for rotating equipment based on an improved sparse dictionary.
Background
With the progress of society, the use of green new energy is more and more taken attention, especially the huge potential of nuclear power receives great attention, but the safety of nuclear power especially needs to be paid attention to when the nuclear power is used, so the requirement on the operation and maintenance of the nuclear power is very high, and the existing nuclear power equipment has a large amount of rotating equipment, the structure of the rotating equipment is complex, and the rotating equipment is easy to break down under the influence of environment and noise. The failure of any kind of rotating equipment may cause the system to be shut down unplanned, and a lot of time is required for workers to perform fault location, maintenance, order replacement parts and the like, so that huge economic losses are generated. The rotating equipment internally comprises components such as a motor, a bearing, a pump and the like, and when the bearing and the gear are in failure, a plurality of transient impact response components appear in a vibration signal. Due to the manufacturing error and the installation error of the equipment, frequency components related to the shaft rotation frequency exist in the vibration signal, so that the fault signal can be accurately separated under the conditions of strong noise and various interferences, and the method has important significance in monitoring the state of a rotary mechanical system.
In recent years, with the depth of machine learning research, a data model is gradually applied to fault diagnosis of rotating equipment, and a common method is to perform feature extraction on acquired vibration signals, label data of different fault types, and then realize data classification through various classifier methods, such as a support vector machine, nearest neighbor, a neural network, a decision tree and the like. The characteristic parameter extraction method comprises the steps of extracting time domain characteristic parameters and frequency domain characteristic parameters, processing vibration signals by using signal processing methods such as envelope spectrum analysis, wavelet analysis, empirical mode decomposition and the like to obtain component signals, and then extracting time domain and frequency domain characteristic parameters on different component signals, wherein the sparse decomposition method realizes sparse expression of the signals by constructing an over-complete dictionary, and has certain anti-noise capacity and weak signal extraction capacity.
For example, Chinese patent invention patent CN111678691A discloses a gear fault detection method based on an improved sparse decomposition algorithm, relating to the technical field of fault detection, the method is based on the traditional sparse reconstruction based on the parameter dictionary, the signal preprocessing and the optimized design of the parameter dictionary are added, the signal preprocessing is realized by combining dual-tree complex wavelet decomposition with the maximum kurtosis principle, the influence of noise on subsequent processing is greatly reduced, the target characteristic parameters are determined based on the relevant filtering of the Laplace wavelet, so that an over-complete dictionary is constructed, the redundancy of the dictionary can be effectively reduced, the designed dictionary is more similar to fault characteristics, finally, the extraction of the impact characteristics in the vibration signal is realized by combining a matching pursuit algorithm, the fault detection is realized, the calculation efficiency of sparse representation can be improved, and the effective fault diagnosis is realized.
However, the above fault detection method still has the following disadvantages: although the optimal overcomplete dictionary is preferred, the same overcomplete dictionary is still used for all band signals, some key signals cannot be effectively extracted, the signal reconstruction precision is reduced, the key signals are lost, and the equipment state evaluation cannot be effectively realized.
Therefore, how to design a fault diagnosis method or system for rotating equipment based on an improved sparse dictionary becomes a problem which needs to be solved urgently at present.
Disclosure of Invention
The invention aims to provide a fault diagnosis method for rotary equipment based on an improved sparse dictionary, which is convenient to execute, extracts fault signals by the improved sparse dictionary method, reserves key characteristic signals, establishes different fault models according to fault types, finally realizes fault diagnosis according to a classification method and effectively improves the equipment fault diagnosis accuracy.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
a fault diagnosis method for rotary equipment based on an improved sparse dictionary comprises the following steps:
s1: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
s2: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
s3: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
s4: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
Preferably, in step S1, the predetermined value k is not less than 2.
Preferably, the step S2 is executed by the following steps:
s2.1: constructing initial optimal related atoms based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining an algorithm parameter of dictionary learning, and constructing an over-complete dictionary, namely an over-complete atom library;
s2.2: fixing the constructed over-complete dictionary, and determining an optimal sparse coefficient;
s2.3: fixing sparse coefficients, updating the dictionary column by column to minimize residual errors, and finally determining an optimal dictionary to obtain a reconstructed sparse expression;
s2.4: repeating the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse expression corresponding to each component signal;
s2.5: and combining the optimal sparse coefficient with the corresponding optimal dictionary, and respectively carrying out sparse reconstruction on each component signal.
As a preferred aspect of the present invention, when step S2.2 is executed, after the overcomplete dictionary is fixed, the sparse coding is adjusted so that the error of the target function is minimized, and then the optimal sparse coefficient can be determined.
Preferably, the step S3 is executed by the following steps:
s3.1: calculating a time domain waveform kurtosis index on each scale of the component signal;
s3.2: calculating a kurtosis index value of an envelope demodulation spectrum;
s3.3: according to the two kurtosis values, calculating to obtain an impact sparsity, and reconstructing the component signals according to the impact sparsity;
s3.4: steps S3.1 to S3.3 are repeatedly performed, reconstructing each component signal.
Preferably, when step S3.3 is executed, corresponding weight values of the two kurtosis indicators are set, the two weight values are added to be equal to one, the two weight values are respectively multiplied by the waveform kurtosis indicator and the kurtosis indicator to obtain two kurtosis values, and the impulse sparseness is calculated according to the two kurtosis values.
Preferably, the step S4 is executed by the following steps:
s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the characteristic parameters comprise effective values, peak values, kurtosis indexes, waveform indexes and frequency domain characteristic parameters;
s4.2: establishing a training set and a test set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining the optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and identifying faults.
Preferably, the method of training the training set when performing step S4.2 includes using gaussian process classification.
On the other hand, the invention also provides a rotating equipment fault diagnosis system based on the improved sparse dictionary, which comprises the following steps:
a preprocessing module: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
a first reconstruction module: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
a diagnostic module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
The rotating equipment fault diagnosis method and system based on the improved sparse dictionary have the beneficial effects that: the method is convenient to execute, fault signal extraction is carried out by improving a sparse dictionary method, key characteristic signals are reserved, different fault models are established according to fault types, finally fault diagnosis is realized according to a classification method, and the equipment fault diagnosis accuracy is effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a fault diagnosis method for rotary equipment based on an improved sparse dictionary according to the present invention;
FIG. 2 is a dictionary parameter of a component signal in an embodiment of a method for diagnosing faults of rotating equipment based on an improved sparse dictionary;
FIG. 3 is a time domain comparison diagram of another component signal in an embodiment of a method for diagnosing faults of rotating equipment based on an improved sparse dictionary;
FIG. 4 is a graph comparing frequency spectrums of another component signal in an embodiment of the method for diagnosing faults of rotating equipment based on the improved sparse dictionary;
FIG. 5 is a vibration signal time domain diagram of a further component signal after reconstruction according to an impact sparsity in an embodiment of the rotating equipment fault diagnosis method based on the improved sparse dictionary;
FIG. 6 is a schematic diagram of the module structure of a rotating equipment fault diagnosis system based on an improved sparse dictionary.
Detailed Description
The following are specific examples of the present invention and further describe the technical solutions of the present invention, but the present invention is not limited to these examples.
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the modules and steps set forth in these embodiments and steps do not limit the scope of the invention unless specifically stated otherwise.
Meanwhile, it should be understood that the flows in the drawings are not merely performed individually for convenience of description, but a plurality of steps are performed alternately with each other.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and systems known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
Example one
As shown in fig. 1 to 5, which are only one embodiment of the present invention, the present invention provides a fault diagnosis method for a rotating device based on an improved sparse dictionary, the method comprising the following steps:
s1: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
in step S1, the predetermined value k is not less than 2, for example, the sampling frequency is 12kHz for the drive end acceleration data collected on the kesselski bearing simulation experiment table at 1797r/min of rotation speed, in this example, the vibration signal is generally decomposed by 3-layer wavelet packet to obtain 8 component signals of equal frequency bands.
S2: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
in this embodiment, when step S2 is executed, the method specifically includes the following steps:
s2.1: constructing initial optimal related atoms based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining an algorithm parameter of dictionary learning, and constructing an over-complete dictionary, namely an over-complete atom library;
s2.2: fixing the constructed over-complete dictionary, and determining an optimal sparse coefficient;
s2.3: fixing sparse coefficients, updating the dictionary column by column to minimize residual errors, and finally determining an optimal dictionary to obtain a reconstructed sparse expression;
s2.4: repeating the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse expression corresponding to each component signal;
s2.5: and combining the optimal sparse coefficient with the corresponding optimal dictionary, and respectively carrying out sparse reconstruction on each component signal.
Furthermore, when step S2.2 is performed, after the overcomplete dictionary is fixed, the sparse coding is adjusted so that the error of the objective function is minimized, and the optimal sparse coefficient can be determined.
It should be noted that the parameters of the optimal dictionary are different for each component signal, and as shown in fig. 2, the parameters of the optimal dictionary are obtained from the first component signal in step S1.
In step S2.5, the component signals are respectively sparsely reconstructed to obtain a time domain contrast map and a spectrum contrast map before and after the sparse reconstruction of each component signal, as shown in fig. 3 and 4, which facilitates step S3.
S3: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
in this embodiment, when step S3 is executed, the method specifically includes the following steps:
s3.1: calculating a time domain waveform kurtosis index K on each scale of the component signal, wherein the specific calculation scores are as follows:
wherein
Is the time domain average of the component signals,
is the fourth power of the time domain average,
is the standard deviation of the component signals.
S3.2: calculating a kurtosis index value Ke of an envelope demodulation spectrum, wherein the specific calculation scores are as follows:
wherein:
a Hilbert transform representing the signal;
s3.3: calculating to obtain an impact sparsity S according to the two kurtosis values, and reconstructing the component signals according to the impact sparsity;
here, corresponding weight values α and β of two kurtosis indicators are set, the two weight values are added to be equal to one, that is, α + β =1, the two weight values are multiplied by a waveform kurtosis indicator and a kurtosis indicator value respectively to obtain two kurtosis values, and a shock sparseness S is calculated from the two kurtosis values, and the calculation formula is as follows:
both may be set to 0.5 and the reconstructed signals are as shown in fig. 5;
s3.4: steps S3.1 to S3.3 are repeatedly performed, reconstructing each component signal.
S4: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
In this embodiment, when step S4 is executed, the method specifically includes the following steps:
s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the extraction of the characteristic parameters comprises but is not limited to effective values, peak values, kurtosis indexes, waveform indexes and frequency domain characteristic parameters, and 15 characteristic parameters are generally extracted;
s4.2: establishing a training set and a test set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining the optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and identifying faults.
Also, the method of training the training set when performing step S4.2 includes using gaussian process classification.
The number of samples is 1331, wherein the number of normal samples 121, the number of rolling element fault samples 241, the number of inner ring fault samples 242 and the number of outer ring fault samples 727 are included. Randomly shuffled to select 931 groups of data as training samples and 400 groups of data as test samples. And training the training set by using a Gaussian process classification method, wherein the kernel function is selected as a Gaussian kernel function, and the parameter is selected to be 1.
Finally, 70 inner ring fault samples are detected, the number of accurate diagnosis is 70, and the diagnosis accuracy is 100%; 57 rolling element fault samples are detected, the diagnosis accuracy is 57, and the diagnosis accuracy is 100%; 231 outer ring fault samples are detected, the diagnosis accuracy is 231, and the diagnosis accuracy is 100%; 42 normal samples are detected, the number of accurate diagnoses is 42, and the diagnosis accuracy is 100%. Therefore, the method provided by the invention can effectively improve the accuracy of identifying the fault of the rotary mechanical equipment.
The fault diagnosis method for the rotating equipment based on the improved sparse dictionary is convenient to execute, fault signals are extracted through the improved sparse dictionary method, key characteristic signals are reserved, different fault models are established according to fault types, finally fault diagnosis is realized according to a classification method, and the fault diagnosis accuracy of the equipment is effectively improved.
Example two
As shown in fig. 6, the present invention is only one embodiment of the present invention, and the present invention further provides a system in which the method for diagnosing a fault of a rotating device based on an improved sparse dictionary may be implemented in the above embodiment, the system including:
a preprocessing module: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
a first reconstruction module: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
a diagnostic module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
The method and the system for fault diagnosis of the rotating equipment based on the improved sparse dictionary are convenient to execute, the fault signal is extracted by the improved sparse dictionary method, the key characteristic signal is reserved, different fault models are established according to the fault types, and finally the fault diagnosis is realized according to the classification method, so that the fault diagnosis accuracy of the equipment is effectively improved.
While certain specific embodiments of the present invention have been described in detail by way of illustration, it will be understood by those skilled in the art that the foregoing is illustrative only and is not limiting of the scope of the invention, as various modifications or additions may be made to the specific embodiments described and substituted in a similar manner by those skilled in the art without departing from the scope of the invention as defined in the appending claims. It should be understood by those skilled in the art that any modifications, equivalents, improvements and the like made to the above embodiments in accordance with the technical spirit of the present invention are included in the scope of the present invention.
Claims (9)
1. A fault diagnosis method for rotary equipment based on an improved sparse dictionary is characterized by comprising the following steps:
s1: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
s2: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
s3: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
s4: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
2. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 1, wherein the method comprises the following steps:
when step S1 is executed, the predetermined value k is not less than 2.
3. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 1, wherein the method comprises the following steps:
when step S2 is executed, the method specifically includes the following steps:
s2.1: constructing initial optimal related atoms based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining an algorithm parameter of dictionary learning, and constructing an over-complete dictionary, namely an over-complete atom library;
s2.2: fixing the constructed over-complete dictionary, and determining an optimal sparse coefficient;
s2.3: fixing sparse coefficients, updating the dictionary column by column to minimize residual errors, and finally determining an optimal dictionary to obtain a reconstructed sparse expression;
s2.4: repeating the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse expression corresponding to each component signal;
s2.5: and combining the optimal sparse coefficient with the corresponding optimal dictionary, and respectively carrying out sparse reconstruction on each component signal.
4. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 1, wherein the method comprises the following steps:
and S2.2, after the over-complete dictionary is fixed, the sparse coding is adjusted to ensure that the error of the target function is minimum, and the optimal sparse coefficient can be determined.
5. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 1, wherein the method comprises the following steps: when step S3 is executed, the method specifically includes the following steps:
s3.1: calculating a time domain waveform kurtosis index on each scale of the component signal;
s3.2: calculating a kurtosis index value of an envelope demodulation spectrum;
s3.3: according to the two kurtosis values, calculating to obtain an impact sparsity, and reconstructing the component signals according to the impact sparsity;
s3.4: steps S3.1 to S3.3 are repeatedly performed, reconstructing each component signal.
6. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 5, wherein the method comprises the following steps:
and when the step S3.3 is executed, setting corresponding weight values of the two kurtosis indexes, adding the two weight values to be equal to one, multiplying the two weight values by the waveform kurtosis index and the kurtosis index respectively to obtain two kurtosis values, and calculating according to the two kurtosis values to obtain the impact sparsity.
7. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 1, wherein the method comprises the following steps:
when step S4 is executed, the method specifically includes the following steps: s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the characteristic parameters comprise effective values, peak values, kurtosis indexes, waveform indexes and frequency domain characteristic parameters;
s4.2: establishing a training set and a test set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining the optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and identifying faults.
8. The method for diagnosing the fault of the rotating equipment based on the improved sparse dictionary as claimed in claim 7, wherein the method comprises the following steps:
in performing step S4.2, the method of training the training set includes using gaussian process classification.
9. A rotating equipment fault diagnosis system based on an improved sparse dictionary is characterized by comprising:
a preprocessing module: obtaining a vibration signal of a fault of the rotating equipment, and carrying out preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of 2 k-th power of equal frequency bands;
a first reconstruction module: constructing an overcomplete atom library of the vibration signals to obtain sparse coefficients in the overcomplete atom library, obtain reconstruction sparse expressions, and performing single-branch reconstruction on each component signal;
a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
a diagnostic module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and identifying the fault.
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