Loom fault diagnosis method based on fusion of expert system and neural network algorithm

Abstract

The invention relates to a loom fault diagnosis method based on fusion of an expert system and a neural network algorithm. According to the method, the neural network is used for fault recognition of the rapier loom, and the neural network is cooperatively coupled with an expert system, so that intelligent judgment of fault diagnosis of the rapier loom is realized. According to the fault diagnosis method, real-time monitoring and fault diagnosis can be well carried out on the running state of the running rapier loom equipment, and equipment faults can be found in time; the method can effectively prolong the service life of equipment, judge the fault position, improve the stability of the equipment, and ensure the safety and the reliability of the equipment in the whole life cycle.

Description

technical field [0001] The invention relates to the technical field of rapier loom fault diagnosis, in particular to a loom fault diagnosis method based on the fusion of an expert system and a neural network algorithm. Background technique [0002] Enterprises have put forward higher requirements for production efficiency and product quality. Improving the intelligence level of traditional manufacturing has become the general trend. Interconnection of workshop equipment and workshop networking are one of the prerequisites for intelligent manufacturing. The remote monitoring and fault diagnosis system of rapier looms is of great significance to realize the interconnection of workshop equipment, workshop networking functions and improve the level of intelligent fault diagnosis of rapier looms. [0003] With the rapid development of computer technology, signal processing technology and artificial intelligence textile industry, intelligent fault diagnosis of rapier looms has beg...

AI Technical Summary

Problems solved by technology

This system puts the design of the inference engine and knowledge base of the expert system on the PC, but it can only seek answers in limited customized rules. When dealing with a complex and irregular knowledge base, the traditional expert system still cannot converge.

The traditional expert system has no self-learning function. When it encounters an incompatible type of database, it cannot handle it and will fall into a loop without termination

The introduction of neural network just solved this problem, and the method of combining neural network and expert system on rapier looms has not been reported yet.

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