Process industrial fault diagnosis method based on bidirectional long-short-term neural network

Abstract

The invention discloses a process industry fault diagnosis method based on a bidirectional long-short-term neural network, and the method comprises the following steps: S1, carrying out data set preparation, wherein a TE model introduces a fault from 160 groups of data, and a data set is used for building a monitoring model; S2, carrying out feature extraction, wherein the feature extraction adopts data feature extraction based on a gradient elevator, and an additive model composed of a plurality of classifiers is found out in the gradient descent direction; S3, establishing an experiment platform; S4, carrying out an experiment: building a bidirectional long-short-term neural network model by adopting a Keras framework, wherein a research object is a Tennician Eastman model; and S5, giving out experimental results. According to the method, the strong generalization ability of the bidirectional long-short-term neural network is utilized, the defects of gradient disappearance and gradient explosion of a long sequence can be avoided, and the problems of low accuracy, frequent missing report and false report phenomena and low generalization ability in process industry fault diagnosisare solved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a process industry fault diagnosis method based on a bidirectional long-short time neural network. Background technique [0002] With the rapid development of computer technology and modern industry, industry tends to be more and more intelligent and complex. Higher requirements are put forward for the safety of the production process. The occurrence of failure will cause a large number of casualties and property losses. Therefore, it is particularly important to detect or diagnose system failures in time. The development of fault diagnosis has gone through three processes. The first stage mainly relies on the experience, senses and simple data of experts and maintenance personnel. Because the production equipment in this stage is relatively simple, fault diagnosis and monitoring are also relatively simple. In the second stage, with the development of sensor and signal...

AI Technical Summary

Problems solved by technology

[0004] The disadvantages of traditional process industry fault diagnosis methods are low accuracy, frequent false negatives and false negatives, and low generalization ability. With the improvement of technical means, a large amount of fault data can be saved. Process Industry Fault Diagnosis Method Based on Neural Network

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