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

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
System Safety Dept, Beijing Branch, CASCO SIGNAL LTD, Beijing 100160, China
3
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
4
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
5
Beijing Institute of Control Engineering, Beijing 100195, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2786; https://doi.org/10.3390/app9142786
Received: 27 May 2019 / Revised: 1 July 2019 / Accepted: 8 July 2019 / Published: 11 July 2019
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PDF [1362 KB, uploaded 11 July 2019]
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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. View Full-Text
Keywords: machining equipment reliability; condition monitoring technology; multi-source information machining equipment reliability; condition monitoring technology; multi-source information
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Dai, W.; Sun, J.; Chi, Y.; Lu, Z.; Xu, D.; Jiang, N. Review of Machining Equipment Reliability Analysis Methods based on Condition Monitoring Technology. Appl. Sci. 2019, 9, 2786.

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