CN113656919B - Asymmetric rotor displacement field reconstruction method based on deep convolutional neural network - Google Patents
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Abstract
The invention discloses an asymmetric rotor displacement field reconstruction method based on a deep convolutional neural network, which comprises the following steps: collecting vibration signals at all measuring points based on a finite element method and a numerical integration method; establishing an asymmetric rotor displacement field reconstruction database based on equal length overlapping sampling; dividing a training set and a verification set based on data normalization; establishing a feature extractor and a signal generator based on a convolutional neural network, bicubic interpolation up-sampling and a fully-connected neural network; training a model based on the mean square error, the Adam algorithm and a learning rate step-down method to obtain a final reconstruction model. The reconstruction method of the invention can overcome the defect that the traditional method is difficult to apply to an asymmetric rotor system with nonlinearity, is suitable for any input signal size, has high prediction speed, can overcome the defect that the reconstruction error of the traditional method is obviously increased along with the reduction of the number of measuring points, and is a method very suitable for industrial sites where more measuring points are difficult to arrange.
Description
Technical Field
The invention belongs to the technical field of rotary machine operation monitoring, and particularly relates to an asymmetric rotor displacement field reconstruction method based on a deep convolutional neural network.
Background
In practical industrial production, there is a class of rotor systems with asymmetric structures, such as generators, motors, crankshafts, fans, and cracked rotors. The asymmetric rotor has complex structure and severe working environment, and is easier to break down than the symmetric rotor under the condition of long-time operation. If the failure can not be detected in time, the machine is stopped in an unplanned mode due to the fact that the vibration quantity exceeds the limit value, and the machine is damaged and casualties are caused in a heavy mode. The health monitoring technology has a great propulsion effect on the detection and inhibition of the vibration of the asymmetric rotor, and the displacement field real-time reconstruction technology is a key technology for health monitoring and intelligent vibration control. Therefore, the research on the real-time reconstruction method of the asymmetric rotor displacement field has very important significance for guaranteeing the safe operation of the asymmetric rotor, reducing great economic loss and avoiding the occurrence of disastrous accidents.
The dynamic equation of the asymmetric rotor system has periodic time-varying parameters and is nonlinear, so that the traditional displacement field reconstruction method, such as a modal expansion method, is difficult to apply to the asymmetric rotor system. In addition, the modal expansion method has the defect that reconstruction errors are obviously increased along with the reduction of the number of the measuring points, and is not in line with the actual situation that more measuring points are difficult to arrange in an industrial field. In summary, there is a need to develop a new real-time reconstruction method for asymmetric rotor displacement fields.
Disclosure of Invention
The invention aims to provide an asymmetric rotor displacement field reconstruction method based on a deep convolutional neural network. The invention establishes a feature extractor and a signal generator based on a deep learning method, takes displacement signals of a small number of measuring points as input and displacement signals of all nodes as output aiming at an asymmetric rotor, so as to complete the real-time reconstruction of the displacement field of the asymmetric rotor system.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an asymmetric rotor displacement field reconstruction method based on a deep convolutional neural network comprises the following steps:
1) Based on a finite element method, establishing a dynamic equation of an asymmetric rotor system, and obtaining initial vibration displacement signals at all nodes of the rotor through a numerical integration method;
2) Performing equal length cutting on the initial vibration displacement signals, respectively processing signals of all nodes and signals of a small number of measuring points into labels and input signals, and establishing an asymmetric rotor displacement field reconstruction database;
3) Carrying out normalization processing on an asymmetric rotor displacement field reconstruction database to form a sample set, and dividing the sample set into a training set and a verification set according to a set proportion;
4) Based on a convolutional neural network, bicubic interpolation up-sampling and a fully connected neural network, establishing a characteristic extractor and a signal generator which are applicable to any input signal size and used for reconstructing an asymmetric rotor displacement field, enabling an input signal to sequentially pass through the characteristic extractor and the signal generator to obtain an output signal, and defining a residual error of the network based on the output signal;
5) And the final feature extractor and the signal generator are obtained through the reverse transfer of the gradient after training, so that an asymmetric rotor displacement field real-time reconstruction model is formed.
The invention is further improved in that in the step 1), latin hypercube sampling is adopted to sample the fault parameters of the asymmetric rotor system, and vibration displacement signals under various different fault parameters are obtained.
The invention further improves that the data is expanded by adopting an overlapping sampling method, and the sample lengths after overlapping sampling are ensured to be equal in the expansion process.
In the step 3), all samples are mapped to between 0 and 1 by adopting a maximum value and minimum value normalization method, and the processing formula is X= (X-min (X))/(max (X) -min (X)), wherein X represents input signals of all nodes or a small number of measuring points; 80% of the samples were treated as training set (X less,t ,X all,t ) An additional 20% of the samples were treated as validation set and validation set (X less,v ,X all,v )。
In the step 4), considering that the number of measuring points is far smaller than the number of time steps, the feature extractor consists of a bicubic interpolation up-sampling layer, a plurality of one-dimensional convolutional neural networks and a fully-connected neural network; in order to increase the depth of the network, the maximum pooling is performed once every two one-dimensional convolutional neural networks; after the one-dimensional convolution, stopping the one-dimensional convolution operation when the length of the time dimension becomes 1, and mapping the output of the one-dimensional convolution neural network into a feature vector through the fully connected neural network to prevent the feature extractor from losing the feature, wherein the dimension of the feature vector is not smaller than the number of independent variables of the displacement field reconstruction problem.
The invention is further improved in that in the step 4), the signal generator is composed of a fully connected neural network, a plurality of two-dimensional convolution neural networks and a plurality of bicubic interpolation up-sampling layers; the feature vector output by the feature extractor is changed into a one-dimensional long vector through a fully connected neural network, and is changed into a two-dimensional matrix through dimension transformation; the two-dimensional matrix is changed into a two-dimensional matrix with the size enlarged twice through a two-dimensional convolutional neural network with the unchanged size and a bicubic interpolation up-sampling layer with the amplification factor of two; after passing through a plurality of two-dimensional convolutional neural networks and bicubic interpolation upsampling layers, finally passing through a two-dimensional convolutional neural network and a sizing bicubic interpolation upsampling layer, so that the size of a finally output two-dimensional matrix is equal to the matrix size of signals of all nodes.
A further improvement of the invention is that in step 4), in order to prevent network degradation problems, in the signal generator, a residual connection is used after each completion of the two-dimensional convolutional neural network and bicubic interpolation upsampling layer.
In step 5), the error is selected as the mean square error, the optimization algorithm is selected as the Adam algorithm, the initial learning rate is 0.001, the learning rate of the network can be stepped down in the training process, and when the error is continuously updated for many times, iteration is stopped, and the calculation convergence is achieved.
Compared with the prior art, the invention has at least the following beneficial technical effects:
the invention provides an asymmetric rotor displacement field reconstruction method based on a deep convolutional neural network. The method has the advantages that: firstly, the defect that the traditional method is difficult to apply to an asymmetric rotor system with nonlinearity can be overcome; secondly, the method is suitable for any input signal size, has high prediction speed, can realize real-time reconstruction, and has important significance for real-time state monitoring of asymmetric rotor vibration; finally, the method can overcome the defect that the reconstruction error of the traditional method is obviously increased along with the reduction of the number of the measuring points, and is suitable for industrial sites where more measuring points are difficult to arrange.
Drawings
FIG. 1 is a schematic block diagram of a method for reconstructing an asymmetric rotor displacement field based on a deep convolutional neural network in accordance with an embodiment of the present invention;
fig. 2 is a schematic diagram of a network architecture of a feature extractor and signal generator constructed in accordance with the present invention.
Detailed Description
The invention is explained in further detail below with reference to the drawings and the specific embodiments.
Referring to fig. 1, the method for reconstructing an asymmetric rotor displacement field based on a deep convolutional neural network provided by the invention comprises the following steps:
1. based on a finite element method, a dynamic equation of an asymmetric rotor system is established, and initial vibration displacement signals at all nodes of the rotor are obtained through a numerical analysis method.
The method comprises the steps of carrying out finite element meshing and boundary condition application on an asymmetric rotor, generating different fault parameter samples by using Latin hypercube sampling, and further obtaining initial vibration displacement signals of all nodes under different fault parameters based on a Dragon-Gregorian tower method.
2. And performing equal-length cutting on the initial vibration displacement signals, respectively processing signals of all nodes and signals of a small number of measuring points into labels and input signals, and establishing an asymmetric rotor displacement field reconstruction database.
Cutting initial vibration data into data with the length of 64, ensuring the cutting length to be unchanged, and completing data capacity expansion through overlapping sampling; and respectively processing the signals of all the nodes and the signals of a small number of measuring points into input signals of the tag and the deep learning network to form an asymmetric rotor displacement field reconstruction database.
3. And carrying out normalization processing on the asymmetric rotor displacement field reconstruction database to form a sample set, and dividing the sample set into a training set and a verification set according to a set proportion.
Specifically, a maximum value and minimum value normalization method is adopted to map all samples to between 0 and 1, and a processing formula is X= (X-min (X))/(max (X) -min (X)), wherein X represents input signals of all nodes or a small number of measuring points; 80% of the samples were treated as training set (X less,t ,X all,t ) An additional 20% of the samples were treated as validation set and validation set (X less,v ,X all,v )。
4. Based on a convolutional neural network, bicubic interpolation up-sampling and a fully connected neural network, a feature extractor and a signal generator which can be suitable for any input signal size and used for reconstructing an asymmetric rotor displacement field are established, input signals sequentially pass through the feature extractor and the signal generator to obtain output signals, and residual errors of the network are defined based on the output signals.
Specifically, the network structure of the created feature extractor and signal generator is shown in fig. 2, and specifically includes: the feature extractor consists of a bicubic interpolation up-sampling layer, a plurality of one-dimensional convolutional neural networks and a fully-connected neural network; in order to increase the depth of the network, the maximum pooling is performed once every two one-dimensional convolutional neural networks; after the one-dimensional convolution, stopping the one-dimensional convolution operation when the length of the time dimension becomes 1, and mapping the output of the one-dimensional convolution neural network into a feature vector through the fully connected neural network. The input of the feature extractor is vibration signal X of a small number of measuring points less The output is the vibration signature Y. The following table is a network parameter table of the feature extractor, in which the size of the input parameters is (4×m less )×N T Wherein M is less The number of the measuring points is a small number of the measuring points, N T For the number of time domain sampling points, the number of convolution blocks n=ceil (log 8 N T ) Where ceil represents a round up.
The signal generator is then formed by a full connectionThe system comprises a neural network, a plurality of two-dimensional convolutional neural networks and a plurality of bicubic interpolation up-sampling layers; the feature vector output by the feature extractor is changed into a one-dimensional long vector through a fully connected neural network, and is changed into a two-dimensional matrix through dimension transformation; the two-dimensional matrix is changed into a two-dimensional matrix with the size enlarged twice through a two-dimensional convolutional neural network with the unchanged size and a bicubic interpolation up-sampling layer with the amplification factor of two; after passing through a plurality of two-dimensional convolutional neural networks and bicubic interpolation upsampling layers, finally passing through a two-dimensional convolutional neural network and a sizing bicubic interpolation upsampling layer, so that the size of a finally output two-dimensional matrix is equal to the matrix size of signals of all nodes. The input of the signal generator is the characteristic vector Y output by the characteristic extractor, and the vibration signal X is output as all nodes all . The following table is a network parameter table of the signal generator, wherein the size of the input parameter is N Y The size of the output parameter is C×M all ×N T Wherein C is the number of channels, i.e. the number of degrees of freedom, M all N is the number of nodes of all the nodes T For the number of time-domain sampling points, the number of up-sampling blocks n=floor (log 2 M all ) Wherein floor represents a rounding down.
The error is selected as the mean square error, the optimization algorithm is selected as the Adam algorithm, the initial learning rate is 0.001, the learning rate of the network can be reduced stepwise in the training process, the learning rate is reduced by 10 times every 100 steps, and when the error is continuously updated 10 times without obvious update, iteration is stopped, and convergence is calculated.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (5)
1. The asymmetric rotor displacement field reconstruction method based on the deep convolutional neural network is characterized by comprising the following steps of:
1) Based on a finite element method, establishing a dynamic equation of an asymmetric rotor system, and obtaining initial vibration displacement signals at all nodes of the rotor through a numerical integration method;
2) Performing equal length cutting on the initial vibration displacement signals, respectively processing signals of all nodes and signals of a small number of measuring points into labels and input signals, and establishing an asymmetric rotor displacement field reconstruction database;
3) Carrying out normalization processing on an asymmetric rotor displacement field reconstruction database to form a sample set, and dividing the sample set into a training set and a verification set according to a set proportion;
4) Based on a convolutional neural network, bicubic interpolation up-sampling and a fully connected neural network, establishing a characteristic extractor and a signal generator which are applicable to any input signal size and used for reconstructing an asymmetric rotor displacement field, enabling an input signal to sequentially pass through the characteristic extractor and the signal generator to obtain an output signal, and defining a residual error of the network based on the output signal; considering that the number of measuring points is far smaller than the time step number, the feature extractor consists of a bicubic interpolation up-sampling layer, a plurality of one-dimensional convolutional neural networks and a fully-connected neural network; in order to increase the depth of the network, the maximum pooling is performed once every two one-dimensional convolutional neural networks; after a plurality of one-dimensional convolutions, stopping one-dimensional convolution operation when the length of the time dimension becomes 1, and mapping the output of the one-dimensional convolution neural network into a feature vector through a fully connected neural network, wherein the dimension of the feature vector is not less than the number of independent variables of the displacement field reconstruction problem in order to prevent the feature extractor from losing the feature;
the signal generator consists of a fully connected neural network, a plurality of two-dimensional convolutional neural networks and a plurality of bicubic interpolation up-sampling layers; the feature vector output by the feature extractor is changed into a one-dimensional long vector through a fully connected neural network, and is changed into a two-dimensional matrix through dimension transformation; the two-dimensional matrix is changed into a two-dimensional matrix with the size enlarged twice through a two-dimensional convolutional neural network with the unchanged size and a bicubic interpolation up-sampling layer with the amplification factor of two; after passing through a plurality of two-dimensional convolutional neural networks and bicubic interpolation upsampling layers, finally passing through a two-dimensional convolutional neural network and a sizing bicubic interpolation upsampling layer, so that the size of a finally output two-dimensional matrix is equal to the matrix size of signals of all nodes;
in order to prevent the network degradation problem, in the signal generator, residual connection is adopted after a two-dimensional convolutional neural network and a bicubic interpolation up-sampling layer are completed each time;
5) And the final feature extractor and the signal generator are obtained through the reverse transfer of the gradient after training, so that an asymmetric rotor displacement field real-time reconstruction model is formed.
2. The method for reconstructing the asymmetric rotor displacement field based on the deep convolutional neural network according to claim 1, wherein in the step 1), the Latin hypercube sampling is adopted to sample the fault parameters of the asymmetric rotor system, so as to obtain vibration displacement signals under a plurality of different fault parameters.
3. The asymmetric rotor displacement field reconstruction method based on the deep convolutional neural network according to claim 1, wherein the data is expanded by adopting an overlap sampling method, and the sample lengths after overlap sampling are ensured to be equal in the expansion process.
4. The asymmetric rotor displacement field reconstruction method based on the deep convolutional neural network according to claim 1, wherein in the step 3), all samples are mapped to between 0 and 1 by adopting a maximum-minimum normalization method, and the processing formula is x= (X-min (X))/(max (X) -min (X)), wherein X represents input signals of all nodes or a small number of measuring points; 80% of the samples were treated as training set (X less,t ,X all,t ) An additional 20% of the samples were treated as validation set and validation set (X less,v ,X all,v )。
5. The method for reconstructing the asymmetric rotor displacement field based on the deep convolutional neural network according to claim 1, wherein in the step 5), the error is selected as a mean square error, the optimization algorithm is selected as an Adam algorithm, the initial learning rate is 0.001, the learning rate of the network can be stepped down in the training process, and when the error is continuously updated for a plurality of times, iteration is stopped, and calculation convergence is achieved.
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| CN111707458A (en) * | 2020-05-18 | 2020-09-25 | 西安交通大学 | Rotor monitoring method based on deep learning signal reconstruction |
| WO2020244134A1 (en) * | 2019-06-05 | 2020-12-10 | 华南理工大学 | Multi-task feature sharing neural network-based intelligent fault diagnosis method |
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| CN109612708A (en) * | 2018-12-28 | 2019-04-12 | 东北大学 | Based on the power transformer on-line detecting system and method for improving convolutional neural networks |
| WO2020244134A1 (en) * | 2019-06-05 | 2020-12-10 | 华南理工大学 | Multi-task feature sharing neural network-based intelligent fault diagnosis method |
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