Knowledge base construction method oriented to fault diagnosis and fault prediction of numerical control machine tool

Abstract

The invention relates to a knowledge base construction method oriented to fault diagnosis and fault prediction of a numerical control machine tool. The method comprises the following steps of: step 1, performing real-time monitoring on a high-grade turning center through a remote monitoring device, and obtaining multiple groups of vibration data Xj(t) representing different fault types, wherein j is the number of acquired vibration data groups, and n is a positive integer; step 2, orderly executing temporal rough wavelet packet analysis on the multiple groups of vibration data Xj(t), obtaining an energy feature vector T' as a condition attribute, and taking the fault type as a decision attribute to construct a fault knowledge primary decision table; step 3, executing discernibility matrix-based fault feature attribute reduction on the fault knowledge primary decision table to generate a rule and form a knowledge base; and step 4, taking the confidence level of the rule as an evaluation index to measure and evaluate the final rule. The method provided by the invention can provide effective guarantee for fault diagnosis and fault prediction, and can be widely used in the high-grade turning center.

Description

technical field [0001] The invention relates to a method for fault diagnosis and fault prediction, in particular to a method for constructing a knowledge base for fault diagnosis and fault prediction of numerically controlled machine tools for high-grade turning centers. Background technique [0002] The high-end turning center with high speed, precision, compound and multi-axis linkage as the core has become one of the main equipment of modern manufacturing industry. Due to the large-scale, integrated, precise and intelligent characteristics of high-end turning centers in terms of mechanism and function, they often encounter problems such as accuracy degradation and high failure rates during processing. Although the CNC system of the high-end turning center itself can complete the simple fault diagnosis function, it is difficult to predict and diagnose the faults of the mechanical system. Once professionals fail to discover the fault in time, it will cause huge losses to t...

AI Technical Summary

Problems solved by technology

At present, there are still the following problems in the research on the construction of machine tool fault knowledge base at home and abroad: 1. There is a lack of research on the fault knowledge base construction of the dynamic data of high-end turning machining centers. It is necessary to comprehensively analyze the dynamic data and cases of machine tool fault diagnosis, and carry out Research on Acquiring Fault Knowledge Effectively

2. When dealing with massive fault information, many redundant fault features are not only useless for fault diagnosis, but may increase the detection cost and affect the accuracy of fault identification

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