CN114136604B - 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 rotating equipment fault diagnosis method and system 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: obtaining a vibration signal, and carrying out k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2; s2: constructing an overcomplete atom library to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single 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 carrying out fault identification. According to the invention, 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, and finally fault diagnosis is realized according to a 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 rotating equipment fault diagnosis method and system based on an improved sparse dictionary.
Background
Along with the progress of society, the use of green new energy is more and more paid attention to, especially the huge potential of nuclear power is paid great attention to, but the nuclear energy especially needs to pay attention to the safety when in use, and then the operation and maintenance requirements for nuclear power are quite high, and at present, the existing nuclear power equipment is provided with a large number of rotating equipment, and the rotating equipment has a complex structure and is easily broken down due to the influence of environment and noise. Failure of any type of rotating equipment can cause unplanned system downtime, and a great deal of time is required for workers to perform failure location, maintenance, order replacement parts, etc., thus resulting in great economic loss. The rotating equipment internally comprises a motor, a bearing, a pump and other parts, and when the bearing and the gear are in fault, various transient impact response components appear in the vibration signal. Because of manufacturing errors and installation errors of the equipment, frequency components related to shaft rotation frequency exist in vibration signals, accurate separation of fault signals can be achieved under the conditions of strong noise and multiple interferences, and the method has important significance for state monitoring of a rotary mechanical system.
In recent years, along with the deep research of machine learning, a data model is gradually applied to fault diagnosis of rotating equipment, and a common method is to extract characteristics of collected vibration signals, label data of different fault types, and then realize data classification by various classifier methods such as a support vector machine, nearest neighbor, a neural network, a decision tree and the like. The method for extracting the characteristic parameters comprises the steps of extracting time domain characteristic parameters, processing vibration signals by using a signal processing method such as envelope spectrum analysis, wavelet analysis, empirical mode decomposition and the like on the basis of the frequency domain characteristic parameters, and then extracting the time domain characteristic parameters and the frequency domain characteristic parameters on different component signals, wherein the sparse decomposition method realizes sparse expression of the signals by constructing an overcomplete dictionary and has certain anti-noise capability and weak signal extraction capability.
For example, chinese patent invention patent CN111678691a discloses a gear fault detection method based on an improved sparse decomposition algorithm, which relates to the technical field of fault detection, and the method increases pretreatment on signals and optimal design on a parameter dictionary based on traditional parameter dictionary sparse reconstruction, and utilizes dual-tree complex wavelet decomposition to combine with maximum kurtosis principle to realize pretreatment on signals, thereby greatly reducing influence of noise on subsequent treatment, determining target characteristic parameters based on relevant filtering of laplace wavelet to construct an overcomplete dictionary, not only effectively reducing dictionary redundancy, but also enabling the designed dictionary to be more similar to fault characteristics, and finally combining with a matching pursuit algorithm to realize fault detection on extraction of impact characteristics in vibration signals.
However, the above fault detection method still has the following drawbacks: although the optimal overcomplete dictionary is preferred, the same overcomplete dictionary is still used for full-band signals, certain key signals cannot be effectively extracted, the signal reconstruction accuracy is reduced, the key signals are lost, and the equipment state evaluation cannot be effectively realized.
Therefore, how to design a rotating equipment fault diagnosis method or system based on an improved sparse dictionary becomes a current urgent problem to be solved.
Disclosure of Invention
The invention aims to provide the rotating equipment fault diagnosis method based on the improved sparse dictionary, which is convenient to execute, performs fault signal extraction by improving the sparse dictionary method, reserves key characteristic signals, establishes different fault models according to fault types, realizes fault diagnosis according to a classification method, and effectively improves equipment fault diagnosis accuracy.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a rotating equipment fault diagnosis method based on an improved sparse dictionary, the method comprising the steps of:
s1: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
s2: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single 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 carrying out fault identification.
Preferably, the predetermined value k is not less than 2 when step S1 is performed.
As a preferred aspect of the present invention, when step S2 is performed, the method specifically includes the steps of:
s2.1: constructing an initial optimal related atom based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining algorithm parameters of dictionary learning, and constructing an overcomplete dictionary, namely an overcomplete atom library;
s2.2: fixing the constructed overcomplete 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 representation;
s2.4: repeatedly executing the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse representation corresponding to each component signal;
s2.5: and combining the optimal sparse coefficients with the corresponding optimal dictionaries, and performing sparse reconstruction on each component signal.
As a preferred aspect of the present invention, when step S2.2 is performed, after the overcomplete dictionary is fixed, the sparse coding is adjusted so that the objective function error is minimized, and then the optimal sparse coefficient can be determined.
As a preferred aspect of the present invention, when step S3 is performed, the method specifically includes the steps of:
s3.1: calculating the time domain waveform kurtosis index on each scale of the component signal;
s3.2: calculating kurtosis index values of the envelope demodulation spectrum;
s3.3: according to the two kurtosis values, calculating to obtain impact sparsity, and reconstructing a component signal according to the impact sparsity value;
s3.4: steps S3.1 to S3.3 are repeatedly performed, and each component signal is reconstructed.
As a preferable mode of the invention, when the step S3.3 is executed, corresponding weight values of two kurtosis indexes are set, the sum of the two weight values is equal to one, the two weight values are respectively multiplied by the waveform kurtosis index and the kurtosis index value to obtain two kurtosis values, and the impact sparsity is obtained according to the two kurtosis values.
As a preferred aspect of the present invention, when step S4 is performed, the method specifically includes the steps of:
s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the characteristic parameter extraction comprises effective values, peak values, kurtosis indexes, waveform indexes and frequency domain characteristic parameters;
s4.2: establishing a training set and a testing set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and carrying out fault recognition.
Preferably, the training set is trained by using a gaussian process classification method when step S4.2 is performed.
In another aspect, the present invention further provides a rotating equipment fault diagnosis system based on an improved sparse dictionary, the system comprising:
and a pretreatment module: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
a first reconstruction module: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single reconstruction on each component signal;
and a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
and a diagnosis module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and carrying out fault identification.
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 built according to fault types, and finally fault diagnosis is realized according to a classification method, so that the equipment fault diagnosis accuracy is effectively improved.
Drawings
FIG. 1 is a schematic flow chart of a method for diagnosing faults of rotary equipment based on an improved sparse dictionary;
FIG. 2 is a dictionary parameter of a component signal in one embodiment of a rotary equipment fault diagnosis method based on an improved sparse dictionary of the present invention;
FIG. 3 is a time domain contrast plot of another component signal in one embodiment of a rotating equipment fault diagnosis method based on an improved sparse dictionary of the present invention;
FIG. 4 is a spectral contrast plot of another component signal in one embodiment of a rotating equipment fault diagnosis method based on an improved sparse dictionary of the present invention;
FIG. 5 is a time domain diagram of a vibration signal after reconstruction from sparsity of impact for yet another component signal in one embodiment of a rotating equipment failure diagnosis method based on an improved sparse dictionary of the present invention;
fig. 6 is a schematic diagram of a module configuration of a rotary equipment fault diagnosis system based on an improved sparse dictionary of the present invention.
Detailed Description
The following are specific examples of the present invention, and the technical solutions of the present invention are further described, 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 the steps do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the flow in the drawings is not merely performed alone, but a plurality of steps are performed to cross each other for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary 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 should be considered part of the authorization specification where appropriate.
Example 1
As shown in fig. 1 to 5, which are only one embodiment of the present invention, the present invention provides a rotating equipment fault diagnosis method based on an improved sparse dictionary, the method comprising the steps of:
s1: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
here, when step S1 is performed, the predetermined value k is not smaller than 2, for example, for the driving end acceleration data collected on a kesixi bearing simulation experiment table in a state that the rotation speed is 1797r/min, the sampling frequency is 12kHz, and in this case, the vibration signal is generally subjected to 3-layer wavelet packet decomposition, so as to obtain 8 component signals in equal frequency bands.
S2: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single reconstruction on each component signal;
in this embodiment, when executing step S2, the method specifically includes the following steps:
s2.1: constructing an initial optimal related atom based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining algorithm parameters of dictionary learning, and constructing an overcomplete dictionary, namely an overcomplete atom library;
s2.2: fixing the constructed overcomplete 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 representation;
s2.4: repeatedly executing the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse representation corresponding to each component signal;
s2.5: and combining the optimal sparse coefficients with the corresponding optimal dictionaries, and performing sparse reconstruction on each component signal.
Moreover, after the overcomplete dictionary is fixed while step S2.2 is performed, the sparse coding is adjusted so that the objective function error is minimized to determine the optimal sparse coefficient.
It should be noted that the parameters of the optimal dictionary for each component signal are different, and as shown in fig. 2, are parameters of the optimal dictionary obtained for the first component signal in step S1.
When executing step S2.5, sparse reconstruction is performed on each component signal, so as to obtain a time domain contrast chart and a frequency spectrum contrast chart before and after sparse reconstruction of each component signal, as shown in fig. 3 and fig. 4, so that step S3 is conveniently performed.
S3: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
in this embodiment, when executing step S3, 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 score is as follows:
wherein the method comprises the steps ofIs the time-domain average value of the component signal,/>Is the fourth power of the time domain average value,/>Is the standard deviation of the component signals.
S3.2: the kurtosis index value Ke of the envelope demodulation spectrum is calculated, and the specific calculation score is as follows:
wherein:hilbert transform representing a signal;
s3.3: according to the two kurtosis values, calculating to obtain impact sparsity S, and reconstructing a component signal according to the impact sparsity value;
here, corresponding weight values α and β of two kurtosis indexes 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 the waveform kurtosis index and the kurtosis index value respectively to obtain two kurtosis values, and the impact sparsity S is calculated according to the two kurtosis values, wherein the calculation formula is as follows:
each may be set to 0.5 and the reconstructed signal as shown in fig. 5;
s3.4: steps S3.1 to S3.3 are repeatedly performed, and each component signal is reconstructed.
S4: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and carrying out fault identification.
In this embodiment, when executing step S4, the method specifically includes the following steps:
s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the characteristic parameter extraction comprises, but is not limited to, an effective value, a peak value, kurtosis indexes, waveform indexes and frequency domain characteristic parameters, and generally 15 characteristic parameters are extracted in total;
s4.2: establishing a training set and a testing set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and carrying out fault recognition.
Also, in performing step S4.2, the method of training the training set includes using a gaussian process classification method.
The number of samples is 1331, wherein the number of the samples is 121, 241, 242 and 727. 931 sets of data are randomly shuffled and selected as training samples, and 400 sets of data are selected as test samples. Training the training set by using a Gaussian process classification method, wherein a kernel function is selected as a Gaussian kernel function, and a parameter is selected as 1.
Finally, 70 inner ring fault samples are detected, the number of diagnosis is 70, and the diagnosis accuracy is 100%; 57 rolling body fault samples are detected, the number of diagnosis accuracy is 57, and the diagnosis accuracy is 100%; 231 outer ring fault samples are detected, the number of diagnosis accuracy is 231, and the diagnosis accuracy is 100%; 42 normal samples are detected, 42 diagnosis accuracy is achieved, and the diagnosis accuracy is 100%. The method provided by the invention can effectively improve the accuracy of identifying the faults of the rotary mechanical equipment.
The rotary equipment fault diagnosis method based on the improved sparse dictionary is convenient to execute, fault signal extraction is carried out through the improved sparse dictionary method, key characteristic signals are reserved, different fault models are built according to fault types, finally fault diagnosis is realized according to a classification method, and equipment fault diagnosis accuracy is effectively improved.
Example two
As shown in fig. 6, which is only one embodiment of the present invention, the present invention also provides a system in which a rotating equipment fault diagnosis method based on an improved sparse dictionary can be implemented in the above embodiment, the system comprising:
and a pretreatment module: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
a first reconstruction module: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single reconstruction on each component signal;
and a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
and a diagnosis module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and carrying out fault identification.
The rotary equipment fault diagnosis method and system based on the improved sparse dictionary are convenient to execute, fault signal extraction is carried out through the improved sparse dictionary method, key characteristic signals are reserved, different fault models are built according to fault types, finally fault diagnosis is realized according to a classification method, and equipment fault diagnosis accuracy is effectively improved.
While certain specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the foregoing examples are provided for the purpose of illustration only and are not intended to limit the scope of the invention, and that various modifications or additions and substitutions to the described specific embodiments may be made by those skilled in the art without departing from the scope of the invention or exceeding the scope of the invention as defined in the accompanying claims. It should be understood by those skilled in the art that any modification, equivalent substitution, improvement, etc. made to the above embodiments according to the technical substance of the present invention should be included in the scope of protection of the present invention.
Claims (6)
1. The rotary equipment fault diagnosis method based on the improved sparse dictionary is characterized by comprising the following steps of:
s1: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
s2: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single 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: extracting features of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and carrying out fault identification;
when executing the step S2, the method specifically comprises the following steps:
s2.1: constructing an initial optimal related atom based on the fault type of the vibration signal, randomly selecting partial data in the component signal to construct an initial dictionary, determining algorithm parameters of dictionary learning, and constructing an overcomplete dictionary, namely an overcomplete atom library;
s2.2: fixing the constructed overcomplete 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 representation;
s2.4: repeatedly executing the steps S2.1 to S2.3 to obtain an optimal dictionary and a reconstruction sparse representation corresponding to each component signal;
s2.5: combining the optimal sparse coefficients with the corresponding optimal dictionaries, and performing sparse reconstruction on each component signal respectively;
when executing the step S3, the method specifically comprises the following steps:
s3.1: calculating the time domain waveform kurtosis index on each scale of the component signal;
s3.2: calculating kurtosis index of the envelope demodulation spectrum;
s3.3: according to the kurtosis index of the time domain waveform in the step S3.1 and the kurtosis index of the envelope demodulation spectrum in the step S3.2, calculating to obtain impact sparsity, and reconstructing a component signal according to the impact sparsity value;
s3.4: repeatedly executing the steps S3.1 to S3.3, and reconstructing each component signal;
when executing the step S3.3, setting corresponding weight values of two kurtosis indexes, adding the two weight values to be equal to one, multiplying the two weight values by the kurtosis index of the time domain waveform and the kurtosis index of the envelope demodulation spectrum respectively to obtain two kurtosis values, and calculating according to the two kurtosis values to obtain the impact sparsity.
2. The rotary equipment fault diagnosis method based on the improved sparse dictionary of claim 1, wherein:
when step S1 is performed, the predetermined value k is not less than 2.
3. The rotary equipment fault diagnosis method based on the improved sparse dictionary of claim 1, wherein:
and when the step S2.2 is executed, after the overcomplete dictionary is fixed, the sparse coding is adjusted to minimize the error of the objective function, and then the optimal sparse coefficient can be determined.
4. The rotary equipment fault diagnosis method based on the improved sparse dictionary of claim 1, wherein:
when executing the step S4, the method specifically comprises the following steps: s4.1: extracting characteristic parameters of the reconstructed component signals, wherein the characteristic parameter extraction comprises an effective value, a peak value, a kurtosis index, a waveform index and a frequency domain characteristic parameter;
s4.2: establishing a training set and a testing set according to the characteristic parameters and the corresponding fault categories, training the training set, and determining optimal parameters of the model;
s4.3: and testing the test set data, realizing fault classification of the test set data, and carrying out fault recognition.
5. The rotary equipment fault diagnosis method based on the improved sparse dictionary of claim 4, wherein:
in performing step S4.2, the method of training the training set includes using a gaussian process classification method.
6. A rotating equipment fault diagnosis system based on an improved sparse dictionary, comprising:
and a pretreatment module: obtaining a vibration signal of a fault of rotating equipment, and carrying out a preset value k-layer wavelet packet decomposition on the vibration signal to obtain component signals of k equal frequency bands of 2;
a first reconstruction module: constructing an overcomplete atom library of the vibration signal to obtain a sparse coefficient in the overcomplete atom library, obtaining a reconstructed sparse representation, and carrying out single reconstruction on each component signal;
and a second reconstruction module: calculating the impact sparsity of each component signal, and realizing component signal reconstruction according to the impact sparsity;
and a diagnosis module: and extracting the characteristics of each reconstructed component signal, establishing a model according to the fault type of the vibration signal, and carrying out fault identification.
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