Fault classification model and method based on stacked sparse Gaussian Bernoulli restricted Boltzmann machine and reinforcement learning

Abstract

The invention discloses a fault classification model and method based on a stacked sparse Gaussian Bernoulli restricted Boltzmann machine and reinforcement learning. The fault classification model iscomposed of a reinforcement learning Q-learning method and a sparse depth belief network model; an SDBN network is trained layer by layer in a non-supervision manner, then gradient descent training isperformed on the entire network in combination with the reinforcement learning Q-learning method, the network weight is adjusted, the correlation between adjacent sampling points between samples andthe dynamic characteristics of process data on a timing sequence are fully considered, and the ability of the model to extract the features of nonlinear and dynamic data in the process is further improved, so that the accuracy of fault classification is improved. The fault classification model disclosed by the invention can effectively solve the problem of low fault classification accuracy rate caused by the nonlinearity of the process data and the dynamics of the 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 stacked sparse Gaussian Bernoulli restricted Boltzmann machine and reinforcement learning. 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 ...

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