Method and system for diagnosing equipment faults through knowledge graph and Bayesian network

Abstract

The invention provides a method and a system for diagnosing equipment faults through a knowledge graph and a Bayesian network. The system for diagnosing the equipment fault through the knowledge graphand the Bayesian network comprises an input module and a diagnosis module, wherein the input module is used for inputting various data for recording maintenance methods and maintenance history; an OCR module used for identifying characters in the various patterns; an entity identification module used for processing the structured content in the document and identifying the types of the charactersin the structured content; and a professional dictionary module which comprises professional vocabularies and a stop word dictionary, wherein the stop word dictionary comprises some common words without entity meanings. According to the method and the system for diagnosing the equipment fault through the knowledge graph and the Bayesian network, the knowledge graph and the Bayesian network are established, so that the algorithm can dynamically learn, the reasoning precision of the algorithm is continuously improved, various probability data are updated, and the precision of future fault diagnosis is greatly improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a method and system for diagnosing equipment faults through a knowledge map and a Bayesian network. Background technique [0002] Given the complexity of electromechanical equipment, its fault detection and fault source diagnosis often requires years of experience. [0003] Problem to be solved: [0004] Two main technical challenges need to be addressed. First of all, maintenance experience and historical records in the form of natural language are required to form a paradigm that can be queried by computers. Second, complex phenomena need to be reasoned to automatically find fault sources. [0005] The natural language processing of electromechanical equipment fault diagnosis system is different from general natural language processing. [0006] 1) Terminology: There are a large number of terminology in the fault maintenance records of electromechanical equipment, s...

AI Technical Summary

Problems solved by technology

Simple natural language processing cannot judge the cause of the fault

Because the commonly used reasoning methods are not combined with natural language, it is difficult to process fault history data recorded by natural language

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