LAPSE:2024.0251
Published Article
LAPSE:2024.0251
A Full-State Reliability Analysis Method for Remanufactured Machine Tools Based on Meta Action and a Markov Chain Using an Exercise Machine (EM) as an Example
Yueping Luo, Yongmao Xiao
February 19, 2024
The reliability of an RMT can be regarded as an important indicator customers can use to recognize its quality; however, it is difficult to implement a full-state reliability analysis of an RMT due to its complicated structural functions. Therefore, a full-state reliability analysis model is proposed herein based on meta action (MA) and a Markov chain for remanufactured exercise machine tools (REMTs). First, an analysis was carried out on individual levels by integrating the MAU decomposition method, and an MAU fault tree model was established layer by layer for the REMT. Second, full-state modeling was performed in view of the MAU characteristics of the REMT, whose operation processes are divided into MAU normal and failure states. A Markov decision-making process was introduced to integrate MAU states and establish our model, which was solved by means of an analytical method for the evaluation of reliability. Finally, an example of a remanufactured machine tool spindle is given to verify the effectiveness of the method.
Keywords
EM, full state, MA unit (MAU), Markov chain, reliability analysis, RMT
Suggested Citation
Luo Y, Xiao Y. A Full-State Reliability Analysis Method for Remanufactured Machine Tools Based on Meta Action and a Markov Chain Using an Exercise Machine (EM) as an Example. (2024). LAPSE:2024.0251
Author Affiliations
Luo Y: College of Physical Education, Shaoyang University, Shaoyang 422000, China
Xiao Y: School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China; Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun 558000, China; Key Laboratory of Complex Systems and Intelligen
Journal Name
Processes
Volume
11
Issue
9
First Page
2794
Year
2023
Publication Date
2023-09-20
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr11092794, Publication Type: Journal Article
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LAPSE:2024.0251
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doi:10.3390/pr11092794
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Feb 19, 2024
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CC BY 4.0
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[v1] (Original Submission)
Feb 19, 2024
 
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Feb 19, 2024
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Original Submitter
Calvin Tsay
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