Integrated semi-supervised Fisher's discrimination-based industrial process fault classifying method

Abstract

The invention discloses an integrated semi-supervised Fisher's discrimination-based industrial process fault classifying method. In the method, offline modeling is first conducted; non-labeled data is randomly sampled and together with labeled data form a plurality of random training subsets; then semi-supervised Fisher dimensionality reduction is conducted to acquire a plurality of Fisher's discrimination matrixes; sampled data with dimensionality reduction is operated according to a Bayesian statistics method to acquire a series of posterior probability matrixes; the posterior probability matrixes of the labeled data and corresponding labels work as training samples adjacent to a measurement layer fusion algorithm K; during online classification, above semi-supervised Fisher's discrimination classifiers are called to acquire a posterior probability matrix of each online to-be-measured sample; and then the posterior probability matrix is input to a measurement layer fusion K adjacent classifier to acquire a final fault classification result. Compared with other methods, industrial process fault classification effect can be improved, knowledge and operation confidence to the process can be enhanced for operators and automatic implantation of the industrial process can be facilitated.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to an industrial process fault classification method based on 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. Thanks to the continuous development of control technology in the process industry, the distributed control system (DCS) has been widely used in the process industry, and the process industry has collected a large amount of 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 ...

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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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