Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

Xiaoyan Wang

doi:10.4108/eetsis.5785

Authors

Xiaoyan Wang College of Advanced Materials Engineering Zhengzhou Technical College

DOI:

https://doi.org/10.4108/eetsis.5785

Keywords:

cnc machine tools, fault diagnosis, integrated techniques, marine predator algorithm, random forests

Abstract

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.

OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.

METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.

RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.

CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.

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