Fault classification model and method based on sparse Gaussian Bernoulli restricted Boltzmann machine and recurrent neural network

Abstract

The invention discloses a fault classification model and method based on a sparse Gaussian Bernoulli restricted Boltzmann machine and a recurrent neural network. The fault classification model is composed of the sparse Gaussian Bernoulli restricted Boltzmann machine, a long and short time memory recurrent neural network, a perceptron and Softmax output layer; the sparse Gaussian Bernoulli restricted Boltzmann machine can learn nonlinear characteristics of data from unlabeled data, the recurrent neural network can process sequence data well, and solves the problem of gradient disappearance or gradient explosion occurring in a network training process by using a long and short time memory unit, and the perceptron and the Softmax output layer enhance the supervised classification ability of the network. The model of the invention has excellent characteristic extraction and perception ability of nonlinear data and dynamic data, and can well solve the problem of low fault classification accuracy caused by the nonlinearity of process data and the dynamics of fault data.

Description

technical field [0001] The invention belongs to the field of industrial process fault diagnosis and classification, and relates to a fault classification model and method based on a sparse Gaussian Bernoulli restricted Boltzmann machine and a cyclic neural network. Background technique [0002] In process monitoring, when a fault is detected, timely and accurate identification and judgment of the fault type based on the abnormal process sensor data is of vital significance to ensure the safe operation of the industrial process and the high-quality output of the product. Accurate fault classification can help operators further locate the link where the fault occurred and the process variable that caused the fault, and is helpful for fault removal and process recovery. Therefore, fault classification has a position that cannot be ignored in industrial production. [0003] With the increasing scale of modern industry and the increasing complexity of process data, there is often...

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Problems solved by technology

[0005] Aiming at the problems of few labeled samples, strong nonlinearity and strong dynamics in the current industrial process, the present invention proposes a fault classification model and method based on sparse Gaussian Bernoulli Restricted Boltzmann Machine and Recurrent Neural Network. The method combines the sparse Gaussian Bernoulli restricted Boltzmann machine and the long short-term memory recurrent neural network into a SGRBM-LSTM-RNN network, which realizes the accurate classification of faults in industrial processes

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