Fault diagnosis method based on principal component analysis and D-S evidence theory

Abstract

The invention relates to a fault diagnosis method based on principal component analysis and a D-S evidence theory. According to the method, practical operation data obtained from a sensor network are subjected to fault detecting through a principal component analysis method at a first step, faults may happen, then under each principal component model, measured data are subjected to principal component analysis, corresponding low-dimension feature vectors tki (i = 0, 1, ..., n) are obtained, the low-dimension feature vectors are subjected to recognition of a recognizer, the recognition results of the recognizer are used as an evidence of fault information in the evidence theory, an evidence combination rule is used for carrying out fusion computing on the evidence information, the credit assignment section of each fault is obtained, and the fact that corresponding faults happen can be judged according to the maximum credibility principle.

Description

technical field [0001] The invention belongs to the computer field and relates to mathematical modeling, in particular to a fault diagnosis method based on principal component analysis and D-S evidence theory. Background technique [0002] As the research of artificial intelligence, expert system and information fusion continues to deepen, it is inevitable to encounter the problem of how to deal with uncertain information. The remarkable improvement of modern computing power allows us to have a more in-depth study of uncertainty. The calculation of complex problems is no longer the main problem. The key is how to use a mathematical framework (classical probability theory) to fully express uncertainty. The dual definition of uncertainty is as follows: [0003] 1. Aleatory Uncertainty: This type of uncertainty is mainly caused by the randomness of the system, also known as random uncertainty, irreducible uncertainty, objective uncertainty, etc. [0004] 2. Epistemic Uncertai...

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Problems solved by technology

[0016] This method is aimed at the D-S theory method when performing multiple data source associations. As the measurement dimension increases and the number of recursive steps increases, the problem of combinatorial explosion will occur. Although the principal component analysis method can lose the least information Under normal circumstances, the original data is compressed into new low-dimensional data, but the ability of fault separation is weak. A fault diagnosis method combining PCA and D-S evidence theory is proposed.

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