Fault diagnosis optimization method, system and device for numerical control machine tool under multiple working conditions

Abstract

The invention provides a fault diagnosis optimization method, system and device for a numerical control machine tool under multiple working conditions. Through arrangement optimization of numerical control machine tool sensors, effectiveness of data acquisition and utilization is improved, an improved multi-scale entropy algorithm is utilized to extract characteristic information of different states represented by the numerical control machine tools of different time scales, deep-level characteristic information is mined, and differentiation of characteristics among different states is improved; on the basis, ITML-K mean value clustering is used for identifying the working condition of the numerical control machine tool so that a problem of a poor identification effect of a traditional clustering method under the condition of multi-working-condition boundary fuzziness is solved; and finally, an entropy-based regularization function is utilized to solve an overfitting problem occurring when a data-driven numerical control machine tool fault diagnosis model is constructed so that the generalization and accuracy of the numerical control machine tool fault diagnosis model are improved, and the optimization of the numerical control machine tool fault diagnosis model is realized. The method, the system and the device have important help for improving the operation safety and reliability of the numerical control machine tool and improving the fault diagnosis rate of the numerical control machine tool.

Description

technical field [0001] The invention relates to the technical field of numerical control machine tool monitoring, and more specifically relates to a fault diagnosis and optimization method, system and equipment of a numerical control machine tool under multiple working conditions. Background technique [0002] Numerical control machine tool (Computer numerical control machine tools), referred to as "CNC machine tool", is an automatic machine tool equipped with a program control system. The emergence of CNC machine tools has greatly improved the ability and efficiency of machine tool processing, and has been widely used in the manufacturing industry. As the key equipment for metal cutting and material manufacturing, CNC machine tools have complex structures, changing working conditions and various fault forms, and it is difficult to trace the root cause of failure. The quality of the operation status is directly related to the processing level and processing efficiency of the...

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Problems solved by technology

[0005] In view of the above problems, the object of the present invention is to provide a fault diagnosis optimization method, system and equipment for CNC machine tools under multiple working conditions, so as to overcome the problem that the traditional CNC machine tool monitoring sensor layout is usually determined by on-site experience, resulting in low utilization of collected data , in data processing, the difference of traditional feature information is not obvious, which leads to the problem of poor quality of feature information for working condition identification and fault diagnosis, and the defect of poor identification effect of traditional clustering under multi-working condition boundary fuzzy, and the defect of using data-driven The problem of low generalization and low diagnosis rate of the fault diagnosis model of CNC machine tools

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