Method for optimizing fault diagnosis rules based on ant colony optimization algorithm

Abstract

The invention discloses a method for optimizing fault diagnosis rules based on an ant colony optimization algorithm, applied to intelligent fault diagnosis. In the invention, the ant colony optimization algorithm is adopted, the fault diagnosis rules (namely a fault pattern sample data vector) in a system fault characteristic pattern sample library are reduced and optimized by reducing the length of the fault pattern sample data vector, redundant condition entries in the fault diagnosis rules are eliminated, and optimal diagnosis rules with fewer condition entries and higher fault diagnosis accuracy are obtained, thus the accuracy rate for diagnosing the type of a fault in a diagnosis field can be improved. The invention also discloses a method for reducing the fault diagnosis rules.

Description

technical field [0001] The invention relates to an intelligent fault diagnosis method, in particular to a fault diagnosis rule optimization method based on an ant colony optimization algorithm, and belongs to the technical field of pattern recognition. Background technique [0002] The development of fault diagnosis technology has mainly gone through three stages: manual diagnosis, modern diagnosis and intelligent diagnosis. Up to now, fault diagnosis methods can be divided into methods based on analytical model, methods based on signal processing and methods based on knowledge. In recent years, artificial intelligence-based data mining techniques for classification have been widely used in the research of fault detection and diagnosis of complex systems. The neural network realizes the complex nonlinear mapping relationship between faults and symptoms by learning connection weights for expressing fault diagnosis knowledge. [0003] Intelligent diagnostic methods are increa...

AI Technical Summary

Problems solved by technology

[0014] The technical problem to be solved by the present invention is to overcome the shortcomings of existing fault diagnosis methods that the fault diagnosis rules are not simple enough and redundant, resulting in relatively low accuracy of fault category diagnosis, and provide a fault diagnosis rule optimization based on an ant colony optimization algorithm method, through this optimization method, a diagnosis rule with fewer attribute items and higher fault category diagnosis accuracy can be found

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