Fault Classification Method Based on Adaptive Ensemble Semi-Supervised Fisher Discriminant

Abstract

The invention discloses an industrial process fault classification method based on self-adaption integrated semi-supervision Fisher discrimination. The method comprises the steps of when off-line modeling is conducted, firstly conducting off-line modeling on unlabeled data, and constituting a semi-supervision random training subset by combining labeled data with the unlabeled data; when iteration training is conducted on a sub classifier each time, conducting semi-supervision Fisher dimensionality reduction to obtain a Fisher discrimination matrix, and obtaining a posterior probability matrix, a combined weight of the sub classifier and a sample weight of the labeled data during next time iteration with the labeled sample data after dimensionality reduction according to a Bayesian statistics method; adopting the posterior probability matrix of the labeled data and a label of the matrix as a training set of a fusion algorithm K near neighbor; during online classification, calling each sub classifier to obtain the posterior probability matrix of an online sample to be detected, and inputting the posterior probability matrix into a fusion K near neighbor classifier with the weight to obtain a final result. Compared with an existing method, the industrial process fault classification method based on the self-adaption integrated semi-supervision Fisher discrimination improves the fault classification result of an industrial process, and more facilitates automated implementation of the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to a fault classification method based on adaptive integrated semi-supervised Fisher discrimination. Background technique [0002] As an important part of process system engineering, process monitoring technology has great research significance and application value for the core goals of modern process industries such as ensuring process safety and improving product quality. With the continuous development of process industry control technology and the widespread application of Distributed Control System (DCS) in the process industry, the process industry has begun to produce massive process data. Therefore, process monitoring technology based on multivariate statistics and pattern recognition has attracted widespread attention from academia and industry, and has become a research hotspot in the field of process monitoring. In the past two decades, a large number of resear...

AI Technical Summary

Problems solved by technology

But the actual problem is that the performance of semi-supervised learning is not stable, and the performance under specific data may not be as good as that of supervised learning.

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