Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology

Author:

Dai WeiORCID,Sun Jiahuan,Chi Yongjiao,Lu ZhiyuanORCID,Xu Dong,Jiang Nan

Abstract

The condition of mechanical equipment during machining is closely related to the accuracy and roughness of the workpiece. In an intelligent sensing environment, a large amount of multi-source data reflecting status information are generated during processing, and a number of studies have been conducted for machining equipment reliability analysis. In this paper, the reliability analysis method of machining equipment based on condition monitoring technology is taken as the main line. And an up-to-date comprehensive survey of multi-source information during the cutting process, failure physical analysis for signal selection and reliability assessment based on condition information will be provided. Finally, the future challenges and trends will also be presented. It is a feasible and valuable research direction to evaluate the reliability of machining equipment for product quality characteristics.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference118 articles.

1. Study on the cutting force prediction of CNC turning;Chen;Mod. Manuf. Eng.,2010

2. Tool life determination based on the measurement of wear and tool force ratio variation

3. Application of big data processing technology in fault diagnosis and early warning of wind turdine cearbox;Zhang;Autom. Electr. Power Syst.,2016

4. Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

5. Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system

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