Nested iterative fisher discriminant analysis-based fault diagnosis isolation method

Abstract

The invention discloses a nested iterative fisher discriminant analysis-based fault diagnosis isolation method. According to the method, potential information included in process data is fully mined, and variables with effects of different extents on a fault can be effectively distinguished, so that discrimination model is established according to different types respectively, the model precision is improved, the performance of on-line fault diagnosis is greatly improved, the reliability and the credibility of actual on-line fault diagnosis are enhanced, accurate repair of the fault by an industrial engineer is facilitated, and thus safe and reliable operation of actual production and high-quality pursuit of products are guaranteed.

Description

technical field [0001] The invention belongs to the field of chemical process statistical monitoring, in particular to a fault variable isolation method based on nested iterative Fisher discriminant analysis. Background technique [0002] As an important production method in industrial production, chemical process is closely related to people's life, and has been widely used in metallurgy, oil refining, papermaking, leather making and other fields. How to ensure the safety of chemical process production, improve product quality and economic benefits is the focus of attention. With the increasingly complex chemical process, online fault detection and diagnosis are becoming more and more important. Fault diagnosis refers to further judging what kind of fault has occurred after the fault is detected. The fault variable isolation can be used to identify the types of faults that occur, to ensure the safe and reliable operation of production and the high quality of products, so ...

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Problems solved by technology

However, when the online fault diagnosis method based on the traditional Fisher discriminant analysis is applied to the actual chemical process, there are three problems: first, the chemical process data is often highly coupled, which may lead to the singularity of the intra-class scatter matrix, so that it cannot Carry out singular value decomposition to extract potential information of process data

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