CN112329626A - Modulation and deep learning fused equipment fault diagnosis method, system and medium - Google Patents

Modulation and deep learning fused equipment fault diagnosis method, system and medium Download PDF

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CN112329626A
CN112329626A CN202011223732.9A CN202011223732A CN112329626A CN 112329626 A CN112329626 A CN 112329626A CN 202011223732 A CN202011223732 A CN 202011223732A CN 112329626 A CN112329626 A CN 112329626A
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王红军
蒋龙陈
籍永建
陈晓
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Beijing Information Science and Technology University
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Abstract

The invention relates to a modulation and deep learning fused equipment fault diagnosis method, system and medium, which comprises the following steps: decomposing a vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component, and drawing a time-frequency graph; preprocessing the time-frequency diagram to be used as the input of a convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training; the convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network. The method can well extract the characteristics in the time-frequency diagram, effectively improve the fault diagnosis accuracy, and can be widely applied to the technical field of gas turbine fault diagnosis.

Description

Modulation and deep learning fused equipment fault diagnosis method, system and medium
Technical Field
The invention relates to the technical field of fault diagnosis of gas turbines, in particular to a fault diagnosis method of a gas turbine integrating modulation and deep learning.
Background
The gas turbine is widely applied to the fields of ships, special vehicles, power generation equipment and the like as power equipment. The gas turbine has an extremely complicated structure, and the gas turbine rotor system is subjected to high temperature and high pressure and also to strong stress and abrasion, so that the gas turbine rotor system becomes a failure-prone part. When the rotor of the gas turbine runs, the working condition is complex, the frequency conversion change is fast, and the vibration signal of the gas turbine often shows a non-stable frequency modulation characteristic. The time-frequency analysis method such as short-time Fourier transform and wavelet analysis has the problem that the time-frequency expression is not clear and visual enough. The recently developed signal decomposition methods such as EMD and VMD have problems of mode aliasing and poor expression in the time-frequency domain.
The Convolutional Neural Network (CNN) is a typical deep learning method, and automatically extracts deep features of an image through continuous Convolutional layers and pooling layers, so that the method has good image recognition and classification effects, and is widely applied to the field of image recognition. Although many researchers have combined CNN and fault diagnosis, the convolutional neural network has achieved some results in the field of fault diagnosis, the corresponding convolutional neural network-based fault diagnosis model is still lacking for the vibration signals of the gas turbine, and there is still room for further optimization of the time-frequency diagram as input.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a modulation and deep learning integrated equipment fault diagnosis method, system and medium, which can well extract features in a time-frequency diagram and effectively improve fault diagnosis accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme: a modulation and deep learning fused equipment fault diagnosis method comprises the following steps: 1) decomposing a vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component, and drawing a time-frequency graph; 2) preprocessing the time-frequency diagram to be used as the input of a convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training; 3) the convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network.
Further, in the step 1), the vibration signal is decomposed into a plurality of frequency components by using a nonlinear frequency modulation component decomposition method.
Further, the nonlinear frequency modulation component decomposition method comprises the following steps:
1.1) expressing a vibration signal to be decomposed by a polynomial phase signal, constructing a demodulation operator by adopting an estimated demodulation parameter, demodulating the vibration signal to be decomposed into a stable signal, and enabling the demodulated signal to have the most concentrated frequency band expression;
1.2) extracting the corresponding most concentrated frequency band component through a band-pass filter, and performing inverse demodulation on the filtered signal through an inverse transformation operator to obtain a meaningful frequency component;
1.3) repeatedly executing the step 1.2), gradually extracting all frequency components from the original vibration signal until all components are decomposed.
Further, the polynomial phase signal model for the non-chirp signal is defined as:
Figure BDA0002762956130000021
wherein a (t) is the instantaneous amplitude of the signal, ci(i-0, 1 …, k) is the phase parameter, k is the order,
Figure BDA0002762956130000022
is the initial phase of the signal, t represents time, g (t) represents the polynomial phase signal, ti+1Representing the (i + 1) th order time instant, j representing the imaginary part.
Further, using the estimated demodulation parameters to define the demodulation operator Φ-(t, C) and inverse transform operator Φ+(t, C) are respectively:
Figure BDA0002762956130000023
Figure BDA0002762956130000024
wherein the content of the first and second substances,
Figure BDA0002762956130000025
for each of the stages of the demodulation parameters,
Figure BDA0002762956130000026
indicating the demodulation parameters of order k.
Further, demodulation parameters
Figure BDA0002762956130000027
i 1 …, k, the demodulation function gd(t, C) is:
gd(t,C)＝a(t)exp(j(2πc0t+φ0))；
then g isd(t, C) has the most concentrated spectrum, and the signal energy will be concentrated at frequency C0Nearby.
Further, in the step 1.2), defining a spectrum concentration index sci (c):
SCI(C)＝E(|F(gd(t,C))|4)
wherein E (-) is the expectation and F (-) is the Fourier transform;
index estimated by SCI (C) maximum parameter
Figure BDA0002762956130000028
Figure BDA0002762956130000031
Using the maximum peak value of the frequency spectrum of the demodulation signal as the initial estimation parameter
Figure BDA0002762956130000032
And adopts a particle swarm optimization method toAnd estimating parameters for optimizing.
A modulation and deep learning fused device fault diagnosis system comprising: the system comprises a time-frequency diagram drawing module, a fault diagnosis model obtaining module and a fault diagnosis module;
the time-frequency diagram drawing module is used for decomposing the vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component and drawing a time-frequency diagram;
the fault diagnosis model acquisition module is used for preprocessing the time-frequency diagram to be used as the input of the convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training;
the fault diagnosis module convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the above methods.
A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the above-described methods.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. the time-frequency diagram obtained by the nonlinear frequency modulation component decomposition method has better time-frequency expression capability than short-time Fourier transform and wavelet time-frequency diagrams. 2. The convolutional neural network fault diagnosis model based on the nonlinear frequency modulation component decomposition time-frequency diagram can well diagnose the gas turbine rotor system.
Drawings
Fig. 1 is a schematic diagram of a typical structure of a CNN model.
Fig. 2 is a time domain diagram of a vibration signal in an embodiment.
Fig. 3 is a time domain diagram and a magnitude-frequency diagram of each component in the embodiment.
Fig. 4 is a time-frequency diagram of a vibration signal in the embodiment.
Fig. 5 is a time-frequency diagram of the improved vibration signal in the embodiment.
Fig. 6 is a CNN network structure in the embodiment.
FIG. 7 is a schematic view of a gas turbine rotor system configuration.
Fig. 8 is a time-frequency diagram after the gray processing in three states.
FIG. 9 fault diagnosis test results.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
In a first embodiment of the invention, a modulation and deep learning fused gas turbine fault diagnosis method is provided, which uses a nonlinear frequency modulation component decomposition method to perform time-frequency analysis on a gas turbine casing vibration signal and uses a convolutional neural network to perform fault diagnosis on a gas turbine rotor, wherein the gas turbine fault signal is a variable speed non-stationary signal. Specifically, the method comprises the following steps:
1) decomposing a vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component, and drawing a time-frequency graph;
in the present embodiment, the vibration signal is decomposed into a plurality of frequency components by a nonlinear frequency modulation component decomposition method.
2) Preprocessing the time-frequency diagram to be used as the input of a convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training;
in the present embodiment, the preprocessing includes gradation processing, size compression, and the like.
3) The convolutional neural network fault diagnosis model realizes effective diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network, can obtain a good diagnosis effect on the gas turbine rotor fault, and has the test accuracy rate of about 99%.
In the step 1), the nonlinear frequency modulation component decomposition method includes the following steps:
1.1) expressing the vibration signal to be decomposed by a polynomial phase signal, adopting the estimated demodulation parameters to construct a demodulation operator, demodulating the vibration signal to be decomposed into a stable signal, and enabling the demodulated signal to have the most concentrated frequency band expression.
1.2) extracting the corresponding most concentrated frequency band component through a band-pass filter, and performing inverse demodulation on the filtered signal through an inverse transformation operator to obtain a meaningful frequency component;
1.3) repeatedly executing the step 1.2), gradually extracting all frequency components from the original vibration signal until all components are decomposed.
In the step 1.1), the polynomial phase signal model for the non-linear frequency modulation signal is defined as:
Figure BDA0002762956130000041
where a (t) is the instantaneous amplitude of the signal,
Figure BDA0002762956130000051
representing the instantaneous frequency, ci(i-0, 1 …, k) is the phase parameter, k is the order,
Figure BDA0002762956130000052
is the initial phase of the signal. t represents time, g (t) represents polynomial phase signal, ti+1Representing the (i + 1) th order time instant, j representing the imaginary part.
Using estimated demodulation parameters to define the demodulation operator phi-(t, C) and inverse transform operator Φ+(t, C) are respectively:
Figure BDA0002762956130000053
Figure BDA00027629561300000510
wherein the content of the first and second substances,
Figure BDA0002762956130000054
the demodulation parameters of each order are rough estimation values.
Figure BDA0002762956130000055
Indicating the demodulation parameters of order k.
Then the demodulation function gd(t, C) is:
gd(t,C)＝g(t)Φ-(t,C)。 (4)
if the demodulation parameter
Figure BDA00027629561300000511
Then the demodulation function gd(t, C) is:
gd(t,C)＝a(t)exp(j(2πc0t+φ0))。 (5)
at this time, gd(t, C) has the most concentrated spectrum, and the signal energy will be concentrated at frequency C0Nearby.
In the step 1.2), the spectrum concentration index sci (c) is defined:
SCI(C)＝E(|F(gd(t,C))|4) (6)
where E (-) is the expectation and F (-) is the Fourier transform.
Index estimated by SCI (C) maximum parameter
Figure BDA0002762956130000056
Figure BDA0002762956130000057
Using the maximum peak value of the frequency spectrum of the demodulation signal as the initial estimation parameter
Figure BDA0002762956130000058
And optimizing the estimation parameters by adopting a particle swarm optimization method.
Figure BDA0002762956130000059
All phase parameters (c) of the instantaneous frequency can be estimated by equations (6) and (7)0…,ck) The instantaneous frequency f (t) of the signal is the first derivative of the phase parameter,
Figure BDA0002762956130000061
the time-frequency diagram of the signal can be drawn after the instantaneous frequency of the signal is obtained.
In order to facilitate observation of the amplitude and the energy ratio of each frequency component, the line width of each frequency component is defined as the integer of the product of the energy ratio of each component and the line width factor, the change and the energy ratio of each frequency component can be clearly shown in a time-frequency diagram, and the line width factor is set to be 5-10 in order to avoid too wide or too narrow line width.
In the step 2), the Convolutional Neural Network (CNN) is a deep learning Convolutional Neural Network, which is a typical deep Network, and reduces the number of weights through weight sharing, thereby improving the efficiency of model training. The network has the advantages that the network model can be trained by taking the image as input, and the feature extraction process is omitted. The convolutional neural network consists of input layers, alternating convolutional and pooling layers, and fully-connected layers and output layers, as shown in fig. 1. The convolutional neural network automatically extracts deep features of the image through continuous convolutional layers and pooling layers.
The convolutional layer realizes feature extraction through convolution calculation, and obtains a feature image from an input image. Assuming that the gray value of the point (x, y) in the input image P with size M × N is f (x, y), and the convolution kernel with size a × b is K (x, y), the convolution calculation can be expressed as:
Figure BDA0002762956130000062
wherein C (s, t) is a convolution operation matrix of the image P and the convolution kernel K, s is more than or equal to 1 and less than or equal to M-a +1, and t is more than or equal to 1 and less than or equal to N-b + 1. The convolution operation is to form a new image matrix by continuously moving a convolution kernel on the image matrix and performing convolution operation with the corresponding area. The convolution layer mainly has the function of feature extraction, and can enhance signal features and reduce noise through convolution operation.
The pooling layer compresses images obtained by the convolution layer through down-sampling calculation, reduces data dimensionality, reduces calculated amount, and avoids overfitting. The purpose of the fully connected layer is to integrate features into a classification role. The average square error is usually used as a loss function of the output layer, and the parameters of the whole network are updated by adopting an inverse error propagation algorithm of the minimum mean square error principle. The loss function is as follows:
Figure BDA0002762956130000063
where loss is the mean squared error of forward propagation,
Figure BDA0002762956130000064
Xirespectively, the output layer output and the desired output.
In a second embodiment of the present invention, there is provided a modulation and deep learning fused device failure diagnosis system, including: the system comprises a time-frequency diagram drawing module, a fault diagnosis model obtaining module and a fault diagnosis module;
the time-frequency diagram drawing module decomposes the vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component, and draws a time-frequency diagram;
the fault diagnosis model acquisition module is used for preprocessing the time-frequency diagram to be used as the input of the convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training;
the fault diagnosis module convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network.
In a third embodiment of the invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of the first embodiment described above.
In a fourth embodiment of the present invention, there is provided a computing device comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of the first embodiment described above.
Example (b):
the gas turbine rotor is easy to cause faults due to the reasons of installation and positioning accuracy, abrasion in operation and the like, so that the vibration is overlarge, the vibration signal of the gas turbine rotor system contains a lot of state characteristic information in the operation process of the gas turbine, and the operation state of the gas turbine can be found by analyzing the vibration signal.
The method includes the steps that vibration speed signals in the operation process of a certain type of gas turbine are measured through experiments, a section with overlarge vibration is intercepted and analyzed, a time domain diagram of the intercepted signals is shown in figure 2, nonlinear frequency modulation component decomposition is conducted on the signals to obtain 5 components, the time domain diagram and a frequency domain diagram of the 5 components are respectively drawn and shown in figure 3, frequency components with physical significance in the signals can be clearly extracted through the nonlinear frequency modulation component decomposition method, and the problem of mode aliasing is well solved.
The time-frequency diagram drawn by the nonlinear frequency modulation component decomposition method is shown in fig. 4, each line in the time-frequency diagram represents a frequency component, the change of the frequency component can be clearly observed through the time-frequency diagram, and the time-frequency diagram obtained by the method can be found to be clear and visual.
The time-frequency diagram obtained by the nonlinear frequency modulation component decomposition method can obtain a clear and visual time-frequency diagram. However, the amplitude and the energy ratio of each frequency component cannot be observed, so that on the basis of a time-frequency diagram drawn by a nonlinear frequency modulation component decomposition method, the integral of the product of the energy ratio of each component and a line width factor is used as the line width of each frequency component, so that the change and the energy ratio of each frequency component can be clearly observed from the time-frequency diagram, and in order to avoid the line width being too wide or too narrow, the line width factor is set to be 5-10. The improved time-frequency diagram is shown in fig. 5.
And (3) constructing a CNN network for input based on a time-frequency diagram generated by the nonlinear frequency modulation component decomposition method, and converting the time-frequency diagram generated by the nonlinear frequency modulation component decomposition method into a gray-scale diagram with the size of 28 multiplied by 28 pixels in order to reduce the calculated amount. The constructed CNN network structure is shown in fig. 6, and includes an input layer with an image size of 28 × 28, 2 convolutional layers with a convolutional kernel of 5 × 5, 2 pooling layers with a maximum value of 2 × 2, 1 full-link layer, and 1 output layer.
The method comprises the following steps of constructing a non-linear frequency modulation component decomposition time-frequency diagram-convolution neural network fault diagnosis model:
1) carrying out nonlinear frequency modulation component decomposition on the data sample set to obtain a time-frequency diagram;
2) carrying out gray level processing on the obtained time-frequency image to obtain a gray level image, converting the size of the gray level image into a 28 x 28 pixel image, carrying out range normalization on the obtained image, and normalizing the elements to be 0-1;
3) and taking the normalized picture set as input to train a convolutional neural network, and carrying out fault diagnosis by using the trained model.
The embodiments are verified, the principle of the rotor structure of a certain type of dual-rotor gas turbine is shown in fig. 7, and during vibration testing, a speed sensor is respectively installed at the radial positions of the casings of the low-pressure compressor and the high-pressure compressor. In the experiment, the rotating speed of the high-pressure rotor is controlled between 6000r/min and 7800r/min, the sampling frequency is 6000Hz, and the sampling time lasts 8000 s.
In the experiment, the vibration of the low-pressure compressor casing is obvious, so that the data measured by the low-pressure compressor casing is used as a data sample for fault diagnosis. Through later analysis, the gas turbine generates airflow shock excitation fault in the experimental process, and further causes the unbalance fault of the rotor. The obtained vibration data are divided into three types, namely a normal state, an air flow excitation state and an unbalanced state, the signal length of each type of state is 1024 points, 500 training samples and 100 testing samples are selected for each type of state during the test, and the data sample set is shown in table 1.
TABLE 1 data sample set
Figure BDA0002762956130000081
And decomposing the data sample set by the nonlinear frequency modulation component to obtain a time-frequency image, carrying out gray level processing on the obtained time-frequency image subjected to the nonlinear frequency modulation component decomposition, and converting the size of the obtained gray level image into 28 multiplied by 28 pixels, wherein typical gray level images in three states are shown in fig. 8.
And normalizing the obtained picture set to be used as a convolutional neural network constructed by input training, wherein the iteration times are set to be 50 times, and the learning rate is 0.1. The trained convolutional neural network fault diagnosis model is tested by using a test set, the result is shown in fig. 9, the test finds that a very good fault diagnosis effect can be achieved for the CNN network based on the nonlinear frequency modulation component decomposition time-frequency diagram, only three samples are wrongly divided, and the test accuracy is about 99%.
In order to verify the superiority of the proposed method, a short-time Fourier time-frequency graph and a wavelet transform time-frequency graph drawn by the same data set are respectively used as input training CNN networks, test results of a test set are shown in Table 2, and the CNN networks trained on the nonlinear frequency modulation component decomposition time-frequency graphs have the highest test accuracy. The time-frequency diagram obtained by the nonlinear frequency modulation component decomposition method has better time-frequency expression capability, and the fault model based on the convolutional neural network can well extract the characteristics in the time-frequency diagram to realize fault diagnosis.
TABLE 2 test accuracy for different diagnostic models
Figure BDA0002762956130000091
In summary, the invention uses a nonlinear frequency modulation component decomposition method to process signals and construct a time-frequency diagram aiming at the problem of the fault of the gas turbine rotor system, and uses the time-frequency diagram as an input training model of a convolutional neural network, and verifies the effectiveness of the model through testing. The result shows that the time-frequency diagram obtained by the nonlinear frequency modulation component decomposition method has better time-frequency expression capability than the short-time Fourier transform and the wavelet time-frequency diagram; the convolutional neural network fault diagnosis model based on the nonlinear frequency modulation component decomposition time-frequency diagram can well diagnose the gas turbine rotor system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.

Claims (10)

1. A modulation and deep learning fused equipment fault diagnosis method is characterized by comprising the following steps:
1) decomposing a vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component, and drawing a time-frequency graph;
2) preprocessing the time-frequency diagram to be used as the input of a convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training;
3) the convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network.
2. The fault diagnosis method according to claim 1, wherein in step 1), the vibration signal is decomposed into a plurality of frequency components by a nonlinear frequency modulation component decomposition method.
3. The fault diagnosis method according to claim 2, wherein the nonlinear chirp component decomposition method comprises the steps of:
1.1) expressing a vibration signal to be decomposed by a polynomial phase signal, constructing a demodulation operator by adopting an estimated demodulation parameter, demodulating the vibration signal to be decomposed into a stable signal, and enabling the demodulated signal to have the most concentrated frequency band expression;
1.2) extracting the corresponding most concentrated frequency band component through a band-pass filter, and performing inverse demodulation on the filtered signal through an inverse transformation operator to obtain a meaningful frequency component;
1.3) repeatedly executing the step 1.2), gradually extracting all frequency components from the original vibration signal until all components are decomposed.
4. A fault diagnosis method as claimed in claim 3, characterized in that the nonlinear chirp signal is defined by a polynomial phase signal model as:
Figure FDA0002762956120000011
wherein a (t) is the instantaneous amplitude of the signal, ci(i-0, 1 …, k) is the phase parameter, k is the order,
Figure FDA0002762956120000012
is the initial phase of the signal, t represents time, g (t) represents the polynomial phase signal, ti+1Representing the (i + 1) th order time instant, j representing the imaginary part.
5. A method of fault diagnosis as claimed in claim 3, characterized in that the demodulation operator Φ is defined by estimating the demodulation parameters-(t, C) and inverse transform operator Φ+(t, C) are respectively:
Figure FDA0002762956120000013
Figure FDA0002762956120000021
wherein the content of the first and second substances,
Figure FDA0002762956120000022
for each of the stages of the demodulation parameters,
Figure FDA0002762956120000023
indicating the demodulation parameters of order k.
6. The failure diagnosis method according to claim 5, wherein the demodulation parameters
Figure FDA0002762956120000024
Then the demodulation function gd(t, C) is:
gd(t,C)＝a(t)exp(j(2πc0t+φ0))；
then g isd(t, C) has the most concentrated spectrum, and the signal energy will be concentrated at frequency C0Nearby.
7. The method for diagnosing faults according to claim 5, wherein in the step 1.2), the spectral concentration index SCI (C) is defined:
SCI(C)＝E(|F(gd(t,C))|4)
wherein E (-) is the expectation and F (-) is the Fourier transform;
index estimated by SCI (C) maximum parameter
Figure FDA0002762956120000025
Figure FDA0002762956120000026
Using the maximum peak value of the frequency spectrum of the demodulation signal as the initial estimation parameter
Figure FDA0002762956120000027
And optimizing the estimation parameters by adopting a particle swarm optimization method.
8. A modulation and deep learning fused equipment fault diagnosis system is characterized by comprising: the system comprises a time-frequency diagram drawing module, a fault diagnosis model obtaining module and a fault diagnosis module;
the time-frequency diagram drawing module is used for decomposing the vibration signal of the gas turbine casing into a plurality of frequency components to obtain the instantaneous frequency of each frequency component and drawing a time-frequency diagram;
the fault diagnosis model acquisition module is used for preprocessing the time-frequency diagram to be used as the input of the convolutional neural network, and obtaining a convolutional neural network fault diagnosis model through training;
the fault diagnosis module convolutional neural network fault diagnosis model realizes the diagnosis of the gas turbine rotor fault by utilizing the characteristic extraction capability of the convolutional neural network.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
CN202011223732.9A 2020-11-05 2020-11-05 Modulation and deep learning fused equipment fault diagnosis method, system and medium Pending CN112329626A (en)

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