A Method of Industrial Process Fault Diagnosis Based on Bayesian Information Criterion

Abstract

The invention relates to an industrial process fault diagnosis method based on the Bayesian information criterion. The method comprises: collecting normal industrial data and calculating several kindsof detection statistics amounts based on normal data; carrying out fault detection on a to-be-detected sample; expressing a fault isolating task into a combinatorial optimization problem; convertingthe problem into a mixed integer nonlinear programming problem by combining the Bayesian information criterion; on the basis of a forward selection algorithm, simplifying the problem into a mixed integer quadratic programming problem; on the basis of a branch-and-bound algorithm, solving a series of similar mixed integer quadratic programming problem to obtain a fault variable combination causingthe sample fault. The industrial process fault diagnosis method has high universality; and the fault variable can be identified without predetermining a fault direction or a known historical fault data set. When the amplitude of the fault is small, an accurate diagnosis result is obtained. Besides, the combination optimization problem is transformed into the quadratic programming problem with sparse constraints for calculation, so that the computational efficiency is improved substantially.

Description

technical field [0001] The invention relates to the technical field of industrial process fault diagnosis, in particular to an industrial process fault diagnosis method based on Bayesian information criterion. Background technique [0002] Efficient and reliable industrial process monitoring plays an extremely important role in ensuring factory safety, product quality and energy efficiency. Rapid advances in measurement, automation, and computer technology have facilitated the use of data-driven techniques. The two processes of fault detection and fault isolation make up fault diagnosis. Since process variables are often highly correlated, multivariate statistical methods are often used for fault detection. Among them, the PCA method has attracted considerable attention. Although the use of principal component analysis for fault detection is relatively mature, its application in fault isolation still needs extensive research. [0003] Contribution maps are the most commo...

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Problems solved by technology

[0004] The present invention provides an industrial process fault diagnosis method based on Bayesian information criterion to solve the technical problem of poor versatility in the existing fault isolation technology

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