Intelligent fault diagnosis method of numerical control machine tool

Abstract

The invention provides an intelligent fault diagnosis method of a numerical control machine tool. The intelligent fault diagnosis method uses fault tree diagnosis as a base, faults are diagnosed by combining a Bayes probability and rule reasoning method, fault cause is found, and maintenance advices are proposed. The intelligent fault diagnosis method includes: firstly extracting fault information from historical maintenance records and machine tool operation instructions, and establishing a numerical control machine tool fault information knowledge base; searching fault trees belonging to a fault position in the fault information knowledge base according to fault characteristic information, calculating probability of occurrence of each fault tree under the current condition if a plurality of fault trees fit conditions, and determining sequence for diagnosing the fault trees according to size of the probability; and finally judging whether all sub-events of top events of the fault trees occur, returning to a fault treatment method corresponding to the events and serving as a solution if the sub-evens are bottom events of the fault trees, otherwise searching sub-events of the events continuously until finding all bottom events fit the fault characteristic information. The intelligent fault diagnosis method of the numerical control machine tool is rapid in diagnosis speed and accurate and reasonable in diagnosis result.

Description

technical field

[0001] The invention belongs to the field of fault diagnosis and relates to the analysis of characteristic signals of numerical control machine tools and the matching of fault knowledge. Background technique

[0002] Since the intelligent fault diagnosis technology was put forward in the 1980s, the fault diagnosis technology has been continuously developed and improved at home and abroad, and new ideas and new ideas have been put forward. In recent years, fault diagnosis technology has developed very rapidly in the world. Commonly used fault diagnosis methods include nearest neighbor method, gray scale method, fault tree method, fuzzy reasoning method, and using artificial neural network to establish a fault diagnosis model. Scholars and engineers around the world have been perfecting fault diagnosis methods. Highly complex CNC machine tools can create huge economic benefits, but once a failure occurs, on the one hand, downtime will bring economic losses, on...

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