Remaining useful life prediction method for machine tools based on meta-action theory

Zongyi Mu, Yan Ran, Genbao Zhang, Hongwei Wang and Xin Yang

Journal of Risk and Reliability, 2021, vol. 235, issue 4, 580-590

Abstract: Remaining useful life (RUL) is a crucial indictor to measure the performance degradation of machine tools. It directly affects the accuracy of maintenance decision-making, thus affecting operational reliability of machine tools. Currently, most RUL prediction methods are for the parts. However, due to the interaction among the parts, even RUL of all the parts cannot reflect the real RUL of the whole machine. Therefore, an RUL prediction method for the whole machine is needed. To predict RUL of the whole machine, this paper proposes an RUL prediction method with dynamic prediction objects based on meta-action theory. Firstly, machine tools are decomposed into the meta-action unit chains (MUCs) to obtain suitable prediction objects. Secondly, the machining precision unqualified rate (MPUR) control chart is used to conduct an out of control early warning for machine tools’ performance. At last, the Markov model is introduced to determine the prediction objects in next prediction and the Wiener degradation model is established to predict RUL of machine tools. According to the practical application, feasibility and effectiveness of the method is proved.

Keywords: Remaining useful life prediction; meta-action theory; control chart; Markov model; Wiener degradation model (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1748006X211002544 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:235:y:2021:i:4:p:580-590

DOI: 10.1177/1748006X211002544

Access Statistics for this article

More articles in Journal of Risk and Reliability
Bibliographic data for series maintained by SAGE Publications ().