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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 379098, 13 pages
http://dx.doi.org/10.1155/2015/379098
Research Article

Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong

Received 12 June 2015; Accepted 2 November 2015

Academic Editor: Nidhal Rezg

Copyright © 2015 Yihai He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system.