CN111505424A - Large experimental device power equipment fault diagnosis method based on deep convolutional neural network - Google Patents
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Abstract
A method for diagnosing faults of power equipment of a large experimental device based on a deep convolutional neural network comprises the steps of collecting historical fault data according to on-line monitoring data of the power equipment, forming an initial sample set, preprocessing the data to obtain a normalized sample set, carrying out deep mining on hidden fault information by using the deep convolutional neural network, adjusting internal weight parameters of a fault diagnosis model according to the deviation of a predicted fault type and a real fault type of the model, and finally carrying out performance testing on the power equipment fault diagnosis model to further improve the fault diagnosis accuracy of the power equipment fault diagnosis model based on the deep convolutional neural network. The method and the device can accurately judge whether the fault occurs according to the monitoring data of the power equipment, output the fault type, obtain a corresponding fault solution according to the fault type, and realize quick and effective recovery of the power equipment system to the normal working state.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a large experimental device power equipment fault diagnosis method based on a deep convolutional neural network.
Background
At present, the overhaul and maintenance work of power equipment of a large experimental device is carried out in a mode of patrolling on the spot by workers, the workers find work faults on the spot with larger workload, and the probability of finding the faults is lower, so that the plan for overhauling the power equipment is not comprehensive, small faults are difficult to find in time, and serious consequences are caused.
In the prior art, diagnostic methods for power equipment faults are mainly divided into three categories, including mathematical model-based, digital signal-based and machine learning-based. The fault diagnosis method based on the mathematical model is simple and direct, but needs to deeply understand the principle and the structure of the electric power equipment to be analyzed, and the mathematical model is established according to the internal structure and the operation principle, so that the final mathematical model can be directly influenced if the early-stage research is wrong; the method based on digital signal processing needs data characteristics with an external obvious characteristic to be widely applied, so that the method is not suitable for most situations; the method based on machine learning can be widely applied only by requiring that the data features have one external feature, so that the method cannot be applied to most situations; the method based on machine learning can effectively utilize the characteristics of data to learn internal rules, but different classifiers and feature extraction methods need to be selected according to different data characteristics, but the optimal collocation of the feature extraction and the classifiers has no clear theoretical guidance.
Disclosure of Invention
The invention aims to provide a method for diagnosing the fault of the power equipment of a large experimental device based on a deep convolutional neural network, so as to solve the problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a large-scale experimental apparatus power equipment fault diagnosis method based on a deep convolutional neural network comprises the following steps:
s1, collecting historical fault data in an online monitoring system of the power equipment to form an original sample set;
s2, preprocessing the data of the original sample set; carrying out normalization processing on the data in the original sample set to form a normalized sample set; dividing the normalized sample set into a training set and a testing set;
s3, determining the network depth of the model according with the characteristics according to the characteristics of the historical fault data, and establishing a deep convolutional neural network model; the structure establishment of the deep convolutional neural network model comprises the following steps:
s301, selecting a learning framework suitable for the network depth, and building the deep convolutional neural network model;
s302, establishing a key layer of the convolutional neural network according to the deep convolutional neural network model, wherein the key layer comprises a convolutional layer, a pooling layer and a full-connection layer; extending the depth of the key layer, and continuously performing interactive connection to form a depth network;
s303, establishing a classification layer after the convolutional neural network in the step S302; classifying and identifying the faults by using a softmax function as a classifier; the softmax function is as follows:
wherein S isiThe classification result representing the output of the ith neural network, namely the result of the ith sample from the front end of the network, n is the number of nodes of the last layer of the neural network, representing how many classes of the item are classified, fciIs the ith neuron value of the last net;
s4, training the deep convolutional neural network model in the step S3 by using the training set in the step S2 to form a fault diagnosis model;
taking the historical data in the training set as the input of the deep convolutional network, outputting the predicted fault type of the deep convolutional neural network model, and comparing the predicted fault type output by the deep convolutional neural network with the actual fault type corresponding to the input fault data in the training set; adjusting internal parameters of the deep convolutional neural network model according to the deviation value of the predicted fault type and the actual fault type to form the fault diagnosis model capable of accurately classifying the fault types according to fault data;
s5, performing performance test on the fault diagnosis model in the step S4 according to the test set in the step S2, judging whether the fault diagnosis model has problems or not according to the result of the performance test, and if the fault diagnosis model has the problems, repeating the steps S3-S5 until the fault diagnosis model passes the performance test;
and S6, using the fault diagnosis model passing the performance test in the step S5 in actual fault detection, collecting real-time data by using the online monitoring system, and applying the real-time data as input to the fault diagnosis model, wherein the fault diagnosis model outputs a fault type and a fault solution strategy corresponding to the real-time data.
Preferably, the building process of the convolutional layer is as follows: extracting characteristic values in the historical fault data to obtain the size of a feature diagram after convolution, and obtaining an output result after characteristic value extraction is carried out on the historical fault data by a convolution layer; the calculation process of the convolutional layer is as follows:
wherein conv represents the output result of the convolutional layer, namely the characteristic value of the historical fault data; denotes the convolution operator; m represents the number of convolution regions; i denotes the area of the convolution, xiData representing the current input is located in the ith area of the training set in the normalized sample set, k represents a fixed convolution kernel, and b represents the convolution layerThe bias execution unit used, f, represents the activation function.
Preferably, the establishment process of the pooling layer is as follows: and performing further feature extraction on the output result subjected to feature value extraction of the convolutional layer, wherein the calculation process of the pooling layer is as follows:
pool＝pooling(conv)
wherein pool represents the output of the pooling layer, i.e. the characteristic value in the characteristic values extracted from the convolutional layer, pool is a pooling function, and conv represents the characteristic value output by the convolutional layer as the input of the pooling layer.
Preferably, the establishing process of the full connection layer is that the output data of the pooling layer in the tensor form is expanded into a vector; the calculation process of the full connection layer is as follows:
wherein fc represents the output of the neural network, f represents the activation function, pxiInput data for the i-th layer neurons that pool the layer into a vector, wiIs a weight matrix of layer i neurons, biIs the bias execution unit of the ith layer.
The invention has the beneficial effects that: the invention discloses a large experimental device power equipment fault diagnosis method based on a deep convolutional neural network. According to the method, a fault diagnosis model is established on the basis of historical data, the fault diagnosis model is utilized to directly and accurately judge that a fault occurs by monitoring data in an online monitoring system, the fault type of the fault is determined, and a corresponding fault solution type is provided according to the fault type, so that the equipment can quickly and effectively recover a normal working state; the data acquisition method required by the invention is simple, and the obtained conclusion is accurate, so that the method can be suitable for most electric power equipment.
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FIG. 1 is a power equipment fault diagnosis flow diagram;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
A large-scale experimental apparatus power equipment fault diagnosis method based on deep convolutional neural network, gather the historical fault data of the power equipment, and form the initial sample set, and carry on the data preprocessing to the said initial sample set, receive the normalized sample set, then the reasonable dividing method of design divides the sample in the said normalized sample set, form training set and test set of the sample; the samples of the training set are used for training a fault diagnosis model of the deep convolutional neural network: adjusting internal weight parameters of the fault diagnosis model according to the deviation between the predicted fault type and the real fault type of the fault diagnosis model, wherein the sample of the test set is used for performing performance test on the power equipment fault diagnosis model, and further improving the accuracy of fault diagnosis model diagnosis. The implementation process is shown in fig. 1, and specifically comprises the following steps:
s1, collecting fault data and online data of the power equipment by using an online monitoring system to form a perfect original sample set, wherein the original sample set respectively lists fault types, fault occurrence reasons and recovery production strategies of the power equipment;
s2, preprocessing the data of the original sample set, normalizing the data according to a certain rule to form a normalized sample set (x, y), and dividing the normalized sample set into a training set train (x, y) and a test set test (x, y); the training set train (x, y) performs performance training on the deep convolutional neural network model to form a fault diagnosis model of the power equipment, and the test set test (x, y) performs performance test on the fault diagnosis model to judge whether the fault diagnosis model can pass the performance test;
wherein x represents historical data detected by the power equipment in the laboratory, and y represents fault classification according to the condition displayed by the detected data;
s3, according to the characteristics of the power equipment fault data, determining the network depth of a fault diagnosis model according with the characteristics, and establishing a deep convolutional neural network model; the deep convolutional neural network model is constructed by the following steps:
s301, selecting a proper deep learning framework to build a deep convolutional neural network model, wherein the deep learning framework comprises a keras framework or a pyrtch framework;
s302, establishing a key layer of the convolutional neural network according to the deep convolutional neural network model, wherein the key layer mainly comprises a volume base layer, a pooling layer and a full-connection layer;
the convolutional layer is the characteristic information of the historical data extracted by a computer vision method, different characteristic extractions are carried out on the data information input by convolutional check, and the calculation process comprises the following steps:
wherein conv represents the output result of convolution layer, i.e. the characteristic value of history data, represents the convolution operation of convolution process, M represents the number of convolution regions, i represents the number of convolution regions, xiThe i-th area of the current input fault sample is represented, k represents a fixed convolution kernel, the size is usually 3 x 3, b represents a bias execution unit used in a convolution layer, f represents an activation function used after convolution operation, and a relu function is used as the activation function in a convolution neural network, and the expression is as follows:
frelu＝max{0,x}
the relu function represents that the input data is positive, the activation value is taken as the input data, and if the input data is negative, the activation value is taken as 0;
the sizes of the feature maps after convolution are:
wherein S ismapSize, S, of the characteristic diagramxRepresenting the dimension of the input acquired historical data of the laboratory monitoring system, wherein k represents the size of a convolution kernel and is generally 3, stride represents the sliding failure of the convolution kernel in the convolution process, n represents the value of an adjustment size and is generally 1, and if the size is kept unchanged, the value is the same as the k value;
the pooling layer is used for reducing dimensionality, reducing network calculation cost and avoiding the problem of overfitting, further feature extraction is carried out on the feature value output by the convolutional layer, and the working process of the pooling layer is as follows:
pool＝pooling(conv)
wherein, pool represents the output of the pooling layer, pool is a pooling function, and is usually selected as max-pool or AVG-pool, the max-pool represents that the maximum value in each feature sensing domain is used as the final output, AVG-pool represents that the average value in each feature sensing domain is used as the final output, and conv represents that the output of the previous convolution layer is used as the input of the pooling layer;
the process in the convolutional layer and the pooling layer is repeated according to the collected data, so that the deep mining of the historical data collected by the laboratory monitoring system is realized;
the full connection layer expands the output of the pooling layer in the tensor form into a vector and is connected with a subsequent neural network, and the operation process of the neural network is as follows:
where fc denotes the output of the neural network and f is the activation function of the neuron, typically using relu or sigmoid, pxiIs the input data of the i-th layer neuron which is developed into a vector after pooling, wiIs a weight matrix of layer i neurons, biIs the bias execution unit of the ith layer;
extending the depths of the convolution layer, the pooling layer and the full-connection layer according to requirements, so that the convolution layer, the pooling layer and the full-connection layer are in interactive connection;
s303, establishing a classification layer, and performing classification and identification on the last layer of the neural network by using a softmax function as a classifier, wherein the calculation process is as follows:
Sithe classification result representing the output of the ith neural network, namely the result of the ith sample from the front end of the network, n is the number of nodes of the last layer of the neural network, representing how many classes of the item are classified, fciIs the ith neuron value of the last net, and the maximum fc can be calculated by the formulaiValue, meaning belonging to the ith class;
s4, training the deep convolutional neural network model in the step S3 by using the training set in the step S2 to form a fault diagnosis model;
taking the historical data in the training set as the input of the deep convolutional network, outputting the predicted fault type of the deep convolutional neural network model, and comparing the predicted fault type output by the deep convolutional neural network with the actual fault type corresponding to the input fault data in the training set; adjusting internal parameters of the deep convolutional neural network model according to the deviation value of the predicted fault type and the actual fault type to form the fault diagnosis model capable of accurately classifying the fault types according to fault data;
s5, performing performance test on the fault diagnosis model in the step S4 according to the test set in the step S2, judging whether the fault diagnosis model has problems or not according to the result of the performance test, and if the fault diagnosis model has the problems, repeating the steps S3-S5 until the fault diagnosis model passes the performance test;
and S6, using the fault diagnosis model passing the performance test in the step S5 in actual fault detection, collecting real-time data by using the online monitoring system, and applying the real-time data as input to the fault diagnosis model, wherein the fault diagnosis model outputs a fault type and a fault solution strategy corresponding to the real-time data.
Examples
In this embodiment, 80% of the normalized sample set is used as the training set, and 20% is used as the test set. By utilizing the method and the designed artificial intelligence algorithm model, the fault type of the power equipment is accurately diagnosed; the accuracy of the fault diagnosis of the power equipment can reach more than 90%.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention discloses a large experimental device power equipment fault diagnosis method based on a deep convolutional neural network, which is characterized in that a fault diagnosis model is established according to working data of an equipment working state, the working data is deeply mined and hidden by the fault diagnosis model, and the hyper-parameters of the fault diagnosis model are adjusted according to a feedback test result, so that the accuracy of the fault diagnosis of the equipment by the fault diagnosis model is improved. According to the method, a fault diagnosis model is established on the basis of historical data, the fault diagnosis model is utilized to directly and accurately judge that a fault occurs by monitoring data in an online monitoring system, the fault type of the fault is determined, and a corresponding fault solution type is provided according to the fault type, so that the equipment can quickly and effectively recover a normal working state; the data acquisition method required by the invention is simple, and the obtained conclusion is accurate, so that the method can be suitable for most electric power equipment.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (4)
1. A large-scale experimental apparatus power equipment fault diagnosis method based on a deep convolutional neural network is characterized by comprising the following steps:
s1, collecting historical fault data in an online monitoring system of the power equipment to form an original sample set;
s2, preprocessing the data of the original sample set; carrying out normalization processing on the data in the original sample set to form a normalized sample set; dividing the normalized sample set into a training set and a testing set;
s3, determining the network depth of the model according with the characteristics according to the characteristics of the historical fault data, and establishing a deep convolutional neural network model; the structure establishment of the deep convolutional neural network model comprises the following steps:
s301, selecting a learning framework suitable for the network depth, and building the deep convolutional neural network model;
s302, establishing a key layer of the convolutional neural network according to the deep convolutional neural network model, wherein the key layer comprises a convolutional layer, a pooling layer and a full-connection layer; extending the depth of the key layer, and continuously performing interactive connection to form a depth network;
s303, establishing a classification layer after the convolutional neural network in the step S302; classifying and identifying the faults by using a softmax function as a classifier; the softmax function is as follows:
wherein S isiThe classification result representing the output of the ith neural network, namely the result of the ith sample from the front end of the network, n is the number of nodes of the last layer of the neural network, representing how many classes of the item are classified, fciIs the ith neuron value of the last net;
s4, training the deep convolutional neural network model in the step S3 by using the training set in the step S2 to form a fault diagnosis model; taking the historical data in the training set as the input of the deep convolutional network, outputting the predicted fault type of the deep convolutional neural network model, and comparing the predicted fault type output by the deep convolutional neural network with the actual fault type corresponding to the input fault data in the training set; adjusting internal parameters of the deep convolutional neural network model according to the deviation value of the predicted fault type and the actual fault type to form the fault diagnosis model capable of accurately classifying the fault types according to fault data;
s5, performing performance test on the fault diagnosis model in the step S4 according to the test set in the step S2, judging whether the fault diagnosis model has problems or not according to the result of the performance test, and if the fault diagnosis model has the problems, repeating the steps S3-S5 until the fault diagnosis model passes the performance test;
and S6, using the fault diagnosis model passing the performance test in the step S5 in actual fault detection, collecting real-time data by using the online monitoring system, and applying the real-time data as input to the fault diagnosis model, wherein the fault diagnosis model outputs a fault type and a fault solution strategy corresponding to the real-time data.
2. The method for diagnosing the fault of the power equipment of the large-scale experimental device based on the deep convolutional neural network as claimed in claim 1, wherein the building process of the convolutional layer is as follows: extracting characteristic values in the historical fault data to obtain the size of a feature diagram after convolution, and obtaining an output result after characteristic value extraction is carried out on the historical fault data by a convolution layer; the calculation process of the convolutional layer is as follows:
wherein conv represents the output result of the convolutional layer, namely the characteristic value of the historical fault data; denotes the convolution operator; m represents the number of convolution regions; i denotes the area of the convolution, xiData representing the current input is located in the ith area of the training set in the normalized sample set, k represents a fixed convolution kernel, b represents a bias execution unit used in the convolution layer, and f represents an activation function.
3. The method for diagnosing the fault of the power equipment of the large-scale experimental facility based on the deep convolutional neural network as claimed in claim 1, wherein the establishing process of the pooling layer is as follows: and performing further feature extraction on the output result subjected to feature value extraction of the convolutional layer, wherein the calculation process of the pooling layer is as follows:
pool＝pooling(conv)
wherein pool represents the output of the pooling layer, i.e. the characteristic value in the characteristic values extracted from the convolutional layer, pool is a pooling function, and conv represents the characteristic value output by the convolutional layer as the input of the pooling layer.
4. The method for diagnosing the fault of the power equipment of the large-scale experimental facility based on the deep convolutional neural network as claimed in claim 1, wherein the full connection layer is established by expanding output data of the pooling layer in a tensor form into a vector; the calculation process of the full connection layer is as follows:
wherein fc represents the output of the neural network, f represents the activation function, pxiInput data for the i-th layer neurons that pool the layer into a vector, wiIs a weight matrix of layer i neurons, biIs the bias execution unit of the ith layer.
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