CN115034312A - Fault diagnosis method for dual neural network model satellite power supply system - Google Patents
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Abstract
The invention discloses a fault diagnosis method for a dual neural network model satellite power system, which comprises the following steps: s1, collecting satellite power supply system data; s2, preprocessing the data of the satellite power supply system: dividing the acquired data into a multi-dimensional time sequence through a sliding time window, and processing the multi-dimensional time sequence through a trained stacked noise reduction self-encoder to remove invalid interference data; s3, classifying the satellite power system data: classifying the preprocessed data; s4, training a double neural network model: inputting the classified datA type I and datA type II in S3 into A CTCN-GRU model and A TCN-A-GRU model respectively for training; s5, use of the dual neural network model: and (3) inputting the actually acquired data into the trained dual neural network for recognition after S2 and S3, comparing the output of the network model with the state of an actual satellite power supply system, and evaluating the network performance.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of a satellite power supply system, in particular to a fault diagnosis method of a double-neural network model satellite power supply system.
Background
The satellite power supply system is one of core systems of the satellite, is the only energy source of the satellite, is the heart of the satellite, limits the power and the service life of the satellite, and has great influence on the on-orbit reliable operation of the satellite due to the normal operation of the satellite power supply system, meanwhile, the probability of the fault of the satellite power supply system is the largest in all satellite systems, 80% of faults have serious influence on the whole satellite task, and 45% of faults can cause the failure of the whole satellite task. Therefore, timely finding and diagnosing the type of the satellite power supply fault has great significance for guaranteeing the reliable execution of the satellite on-orbit task.
Due to the complexity and variety of satellite working environments and the diversification of fault types of the satellite working environments, the fault diagnosis of the satellite power supply system is not easy. Existing satellite power system fault diagnosis techniques include several types of signal processing based, model based, digital drive based. The diagnosis method based on signal processing comprises wavelet analysis, Fourier transform and other methods, which appear earlier, do not need to accurately model the system, and are simple and convenient to operate, but have limited diagnosis capability on early potential faults, limited capability on mining deep fault characteristics, and no good universality. The diagnosis method based on the model comprises a state detection method, an equivalent space method and other methods, and the technology is developed to be more perfect and successfully applied to engineering, such as Livingstone software introduced by the American national aerospace agency, and provides a fault diagnosis platform based on the model. The method does not need fault data and priori knowledge, has good fault diagnosis capability, but with the more and more complex diagnosis objects, the more and more time and labor are wasted in model building, the external interference is serious, and the transportability is poor. Data-driven based methods include expert systems, fault trees, rough sets, fuzzy theory, bayesian, and neural network methods. The method automatically mines the system behavior mode by analyzing the historical data of the diagnosis object without accurate modeling, and the data mining is usually realized by a machine learning algorithm. The deep learning technology is an important branch of machine learning, wherein a neural network algorithm is already applied to the field of fault diagnosis, but the neural network algorithm is more limited to be used in the field of fault diagnosis of a satellite power supply system, and an attempt stage is still performed. The method does not need to accurately model the system, has stronger capacity of processing data with interference and better portability, and has wide development space.
The existing neural network-based method applied to satellite power failure diagnosis inputs data acquired by a sensor into a path of model for training, and has two defects: firstly, with the development of satellite technology and telemetry technology, the data information which can be acquired becomes huge, so that a longer training time is needed for inputting a large amount of data into one path of model to carry out model training; secondly, the running environment of the satellite is very complex, the input and output data of the satellite power supply system which is partially acquired are influenced by the environment and the running state thereof, the fluctuation is large, the other part of data is quantitatively changed according to the task stage of the satellite, and the two data types are not accurately judged in a dividing mode; and thirdly, when the neural network model is applied to satellites in different working environments or executing different tasks, the training needs to be carried out again, and the model training time needs to be spent again.
Disclosure of Invention
The invention aims to provide a fault diagnosis method for a dual neural network model satellite power system, which can be used for autonomously diagnosing the satellite power system when a fault occurs, and realizing an efficient and reliable autonomous diagnosis process without transmitting telemetering data back to a ground base station.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a double neural network model satellite power system fault diagnosis method comprises the following steps:
step S1, collecting satellite power system data: a large amount of data is needed for training a neural network model, and the data is acquired through sensors distributed in a satellite power system shunt regulator, a charging regulator, a discharging regulator, a bus dereferencing module, a solar cell array and a storage battery pack;
step S2, preprocessing the data of the satellite power system: dividing the acquired data into a multi-dimensional time sequence through a sliding time window, and processing the multi-dimensional time sequence through a trained stacked noise reduction self-encoder to remove invalid interference data;
step S3, classifying the satellite power system data: classifying the preprocessed data, dividing data collected by sensors connected with a shunt regulator, a charge regulator and a discharge regulator into task data sets, dividing data collected by sensors connected with a bus value taking module, a solar cell array and a storage battery into data sets which are greatly influenced by satellite states, respectively recording the data sets as a data type I and a data type II, and preparing for carrying out neural network model training of different data types;
step S4, training the double neural network model: building A CTCN-GRU model and A TCN-A-GRU model, and inputting the datA type I and the datA type II classified in the step S3 into the CTCN-GRU model and the TCN-A-GRU model respectively for training;
step S5, the dual neural network model uses: and (3) carrying out actual fault diagnosis effect test through a neural network model obtained by actual training, inputting the actually acquired data into the trained dual neural network for recognition after the step S2 and the step S3, comparing the output of the network model with the state of an actual satellite power supply system, and evaluating the network performance.
The technical scheme of the invention is further improved as follows: the encoding process formula of the self-encoder in step S2 is as follows:
h m ＝f(Wx m +b)；
wherein the content of the first and second substances,x m training sample set, h, representing a high dimensional space m Representing an output vector set of a hidden layer obtained by an encoder, wherein f represents an activation function from an input layer to the hidden layer, W is a weight matrix, and b is a bias vector;
the decoding process of the self-encoder is formulated as:
wherein the content of the first and second substances,
denotes h m Obtaining a data set with the same dimensionality as the input sample set through inverse transformation, wherein g represents an activation function from a hidden layer to an output layer, W' represents a weight matrix, and d is a bias vector;
the mean square error is adopted as the loss function of the self-encoder, and the expression is as follows:
wherein W, W' represents weight matrix, b and d are offset vectors, m is number of layers, x is sample data,
obtaining the output h of the encoder by setting the iteration number to minimize the loss function for the data with the same dimensionality as the input sample set obtained by inverse transformation of the hidden layer output vector m (ii) a The stacked self-encoder is formed by stacking a plurality of self-encoders.
The technical scheme of the invention is further improved as follows: in step S3, 90% of the datA type i is divided into A training set i and 10% is divided into A testing set i, 90% of the datA type ii is divided into A training set ii and 10% is divided into A testing set ii, and the built CTCN-GRU model and TCN-A-GRU model are respectively subjected to fault diagnosis training by using the training set i and the training set ii.
The technical scheme of the invention is further improved as follows: the CTCN-GRU model building process comprises the following steps: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual connection structure, wherein the convolutional layer is added in front of the causal convolutional layer, and input data is subjected to feature extraction processing through the convolutional layer, the causal convolutional layer, the expansion convolutional layer and the residual connection structure, wherein the operation formula of the expansion convolution is as follows:
wherein x is an input sequence; f is a filter; d is the coefficient of expansion; k is the convolution kernel size; s-di ensures that only convolution operations can be performed on past inputs;
after feature extraction is carried out on the CNN layer, the CNN layer is input into a causal convolutional layer for time-constrained data processing, the obtained data is input into an expansion convolutional layer for expanding a receptive field, the TCN layers are connected with one another by using residual errors, a Dropout layer is used in each layer for regularizing a network, and the data after feature extraction is carried out on the TCN layer is input into a GRU for further processing; wherein, the calculation formula of the output state of the GRU is as follows:
wherein x is t For input at time t, h t Output or state at time t, h t-1 At time t-1, w is the weight, σ is the activation function sigmoid (·),tan h (. smallcircle.) is the activation function, z t 、r t 、g t Is an intermediate variable.
The technical scheme of the invention is further improved as follows: the TCN-A-GRU model building process comprises the following steps: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual error connection structure, data are further input into the GRU for processing after being processed by the TCN, and an attention mechanism is introduced, wherein a calculation formula of the attention mechanism is as follows:
wherein, a i Assigning attention weights; h is i Outputting for a hidden layer; c is the attention mechanism output and i is the number of hidden layers.
The technical scheme of the invention is further improved as follows: and the datA in the test set I and the test set II are respectively input into the trained CTCN-GRU model and the trained TCN-A-GRU model for fault diagnosis.
Due to the adoption of the technical scheme, the invention has the technical progress that:
the invention provides two new deep learning frames and A new diagnostic mode of A dual neural network model, the acquired datA is divided into two datA sets according to the characteristics of the datA, and the fault diagnosis is carried out by adopting A dual model combining an improved time convolution network and A gated recursion unit mixed model (CTCN-GRU model) with A time convolution network and an attention control gated recursion unit mixed model (TCN-A-GRU), so that the single model datA input quantity is reduced, the training time of one diagnostic model can be shortened, the efficiency is improved, and the diagnostic processing with more pertinence can be carried out on different characteristic datA types, and the diagnostic accuracy is improved; meanwhile, when the diagnosis mode is applied to another satellite with the same kind of task data and different operation environment data with the trained satellite or a satellite with the same kind of operation environment data and different task data, only the retraining of the diagnosis model with changed input data can be carried out, and the model training time is saved.
Drawings
FIG. 1 is a schematic diagram of a data classification-based dual-model satellite power failure diagnosis mode model of the invention;
FIG. 2 is a schematic diagram of the CTCN-GRU model of the invention;
FIG. 3 is A schematic diagram of the TCN-A-GRU model of the present invention.
Detailed Description
The present invention is further illustrated in detail below with reference to examples:
as shown in fig. 1, a dual neural network model satellite power system fault diagnosis method includes the following steps:
step S1, collecting satellite power system data: a large amount of data is needed for training a neural network model, and the data is collected by sensors distributed on a satellite power supply system shunt regulator, a charge regulator, a discharge regulator, a bus value taking module, a solar cell array and a storage battery pack;
step S2, preprocessing the data of the satellite power supply system: dividing the acquired data into a multi-dimensional time sequence through a sliding time window, and processing the multi-dimensional time sequence through a trained stacked noise reduction self-encoder to remove invalid interference data; the coding process of the self-encoder is expressed as:
h m ＝f(Wx m +b)；
wherein x is m Training sample set, h, representing a high dimensional space m Representing an output vector set of a hidden layer obtained by an encoder, wherein f represents an activation function from an input layer to the hidden layer, W is a weight matrix, and b is a bias vector;
the decoding process of the self-encoder is formulated as:
wherein the content of the first and second substances,
denotes h m Obtaining a data set with the same dimension as the input sample set through inverse transformation, g represents an activation function from a hidden layer to an output layer, W' represents a weight matrix, and d is a biasVector positioning;
the mean square error is adopted as the loss function of the self-encoder, and the expression is as follows:
wherein W, W' represents weight matrix, b and d are offset vectors, m is number of layers, x is sample data,
obtaining the output h of the encoder by setting the iteration number to minimize the loss function for the data with the same dimensionality as the input sample set obtained by inverse transformation of the hidden layer output vector m (ii) a The stacked self-encoder is formed by stacking a plurality of self-encoders.
Step S3, classifying the satellite power system data: classifying the preprocessed data, dividing data collected by sensors connected with a shunt regulator, a charge regulator and a discharge regulator into task data sets, dividing data collected by sensors connected with a bus value taking module, a solar cell array and a storage battery into data sets which are greatly influenced by satellite states, respectively recording the data sets as a data type I and a data type II, and preparing for carrying out neural network model training of different data types;
step S4, training the dual neural network model: the dual neural network model is A hybrid model of an improved time convolution neural network and A gated recursion unit, which is called CTCN-GRU for short, and A hybrid model of the time convolution neural network and an attention system gated recursion unit, which is called TCN-A-GRU for short;
as shown in FIG. 2, the CTCN-GRU model building process is as follows: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual connection structure, wherein the convolutional layer (CNN) is added before the causal convolutional layer, the sensing field of the model is enlarged while the characteristic that the output which does not influence the time t of the TCN is only related to the time t from the previous layer and the convolution of the earlier elements is not influenced, and the input data is subjected to feature extraction processing through the convolutional layer, the causal convolutional layer, the expansion convolutional layer and the residual connection layer, wherein the operation formula of the expansion convolution is as follows:
wherein x is an input sequence; f is a filter; d is the coefficient of expansion; k is the convolution kernel size; s-di ensures that only convolution operations can be performed on past inputs;
after the characteristics of the CNN layer are extracted, the CNN layer is input into a causal convolutional layer to be subjected to time-constrained data processing, the obtained data is input into an expansion convolutional layer to expand a receptive field, the TCN layers are connected with one another by using residual errors, a Dropout layer is used in each layer to regularize a network, the data subjected to the characteristics extraction by the TCN is input into a GRU to be further processed, and finally a group of data is output, wherein the GRU can make up the defects of long-term memory of the TCN, and the convergence of a long-term memory neural network (LSTM) is quicker, so that the capability of a model on processing long-term and long-term time sequence tasks is improved, and the diagnosis accuracy is further improved; wherein, the calculation formula of the output state of the GRU is as follows:
wherein x is t For input at time t, h t Output or state at time t, h t-1 In the state at time t-1, w is a weight, σ is an activation function sigmoid (·), tanh (·) is an activation function,z t 、r t 、g t is an intermediate variable.
As shown in FIG. 3, the TCN-A-GRU model building process is as follows: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual error connection structure, data are further input into the GRU for processing after being processed by the TCN, and an attention mechanism is introduced, wherein a calculation formula of the attention mechanism is as follows:
wherein, a i Assigning attention weights; h is i Outputting for a hidden layer; c is attention mechanism output, i is the number of hidden layers; the attention mechanism has excellent self-regulation capability, the introduction of the attention mechanism can help to strengthen the contribution of important features to fault diagnosis, the diagnosis error caused by the data characteristics of more external interference and larger fluctuation existing in the data type II is avoided, and a more accurate diagnosis result is obtained by utilizing the self-regulation.
And dividing 90% of the datA type I into A training set I and 10% into A testing set I, dividing 90% of the datA type II into A training set II and 10% into A testing set II, and respectively performing fault diagnosis training on the built CTCN-GRU model and the built TCN-A-GRU model by utilizing the training set I and the training set II.
Step S5, the dual neural network model uses: and (3) carrying out actual fault diagnosis effect test through A neural network model obtained by actual training, respectively inputting datA in the test set I and the test set II into the trained CTCN-GRU model and TCN-A-GRU model for recognition, comparing the output of the network model with the state of an actual satellite power supply system, and evaluating the network performance.
Claims (6)
1. A double neural network model satellite power system fault diagnosis method is characterized by comprising the following steps: the method comprises the following steps:
step S1, collecting satellite power system data: a large amount of data is needed for training a neural network model, and the data is collected by sensors distributed on a satellite power supply system shunt regulator, a charge regulator, a discharge regulator, a bus value taking module, a solar cell array and a storage battery pack;
step S2, preprocessing the data of the satellite power supply system: dividing the acquired data into a multi-dimensional time sequence through a sliding time window, and processing the multi-dimensional time sequence through a trained stacked noise reduction self-encoder to remove invalid interference data;
step S3, classifying the satellite power system data: classifying the preprocessed data, dividing data collected by sensors connected with a shunt regulator, a charge regulator and a discharge regulator into task data sets, dividing data collected by sensors connected with a bus value taking module, a solar cell array and a storage battery into data sets which are greatly influenced by satellite states, respectively recording the data sets as a data type I and a data type II, and preparing for carrying out neural network model training of different data types;
step S4, training the dual neural network model: building A CTCN-GRU model and A TCN-A-GRU model, and inputting the datA type I and the datA type II classified in the step S3 into the CTCN-GRU model and the TCN-A-GRU model respectively for training;
step S5, the dual neural network model uses: and (3) carrying out actual fault diagnosis effect test through a neural network model obtained by actual training, inputting the actually acquired data into the trained dual neural network for recognition after the step S2 and the step S3, comparing the output of the network model with the state of an actual satellite power supply system, and evaluating the network performance.
2. The dual neural network model satellite power system fault diagnosis method of claim 1, wherein: the encoding process formula of the self-encoder in step S2 is as follows:
h m ＝f(Wx m +b)；
wherein x is m Training sample set, h, representing a high dimensional space m Representing the output vector set of the hidden layer obtained by the encoder, f representing the activation function from the input layer to the hidden layer, W being the weight matrix, b beingA bias vector;
the decoding process of the self-encoder is formulated as:
wherein the content of the first and second substances,
denotes h m Obtaining a data set with the same dimensionality as the input sample set through inverse transformation, wherein g represents an activation function from a hidden layer to an output layer, W' represents a weight matrix, and d is a bias vector;
the mean square error is used as the loss function of the self-encoder, and the expression is as follows:
wherein W, W' represents weight matrix, b and d are offset vectors, m is number of layers, x is sample data,
obtaining the output h of the encoder by setting the iteration number to minimize the loss function for the data with the same dimensionality as the input sample set obtained by inverse transformation of the hidden layer output vector m (ii) a The stacked self-encoder is formed by stacking a plurality of self-encoders.
3. The dual neural network model satellite power system fault diagnosis method of claim 1, wherein: in step S3, 90% of the datA type i is divided into A training set i and 10% is divided into A testing set i, 90% of the datA type ii is divided into A training set ii and 10% is divided into A testing set ii, and the built CTCN-GRU model and TCN-A-GRU model are respectively subjected to fault diagnosis training by using the training set i and the training set ii.
4. The dual neural network model satellite power system fault diagnosis method of claim 3, wherein: the CTCN-GRU model building process comprises the following steps: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual connection structure, wherein the convolutional layer is added in front of the causal convolutional layer, and input data is subjected to feature extraction processing through the convolutional layer, the causal convolutional layer, the expansion convolutional layer and the residual connection structure, wherein the operation formula of the expansion convolution is as follows:
wherein x is an input sequence; f is a filter; d is the coefficient of expansion; k is the convolution kernel size; s-di ensures that only convolution operations can be performed on past inputs;
after feature extraction is carried out on the CNN layer, the CNN layer is input into a causal convolutional layer for time-constrained data processing, the obtained data is input into an expansion convolutional layer for expanding a receptive field, the TCN layers are connected with one another by using residual errors, a Dropout layer is used in each layer for regularizing a network, and the data after feature extraction is carried out on the TCN layer is input into a GRU for further processing; wherein, the calculation formula of the output state of the GRU is as follows:
wherein x is t For input at time t, h t Output or state at time t, h t-1 At time t-1, w is the weight, σ is the activation function sigmoid (. alpha.), tanh (. alpha.) is the activation function, z is the activation function t 、r t 、g t Is an intermediate variable.
5. The dual neural network model satellite power system fault diagnosis method of claim 3, wherein: the TCN-A-GRU model building process comprises the following steps: the TCN comprises a causal convolutional layer, an expansion convolutional layer and a residual error connection structure, data are further input into the GRU for processing after being processed by the TCN, and an attention mechanism is introduced, wherein a calculation formula of the attention mechanism is as follows:
wherein, a i Assigning attention weights; h is a total of i Outputting for a hidden layer; c is the attention mechanism output and i is the number of hidden layers.
6. The dual neural network model satellite power system fault diagnosis method of claim 3, wherein: and the datA in the test set I and the test set II are respectively input into the trained CTCN-GRU model and the trained TCN-A-GRU model for fault diagnosis.
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