Cost benefit analysis of condition monitoring systems for optimal maintenance decision making

• Main Author: Wang, Yibin
• Other Authors: Wang, Pingfeng
• Format: Others
• Language: en_US
• Published: Wichita State University 2013
• Subjects: Electronic dissertations
• Online Access: http://hdl.handle.net/10057/6847

Tremendous advances in high performance sensing and signal processing technology enable the development of condition monitoring systems (CMS) for complex engineered systems to detect, diagnose, and predict the system-wide effects of failure events. Although employing CMS in preventing catastrophic system failures and reducing the operation and maintenance (O&M) costs have been acknowledged, the cost and benefit of CMS have not been well studied and further the advantages of CMS have not been fully recognized for the optimal maintenance decision making, mainly due to the lack of valid theoretical modeling addressing the interrelationship between the CMS effectiveness and system downtime due to system failures. In this study, a Poisson Process model will be developed for the modeling of occurrence of the system-wide failure events and study the potential benefits provided by the CMS in preventing these failure events. With the developed Poisson process model, the cost benefit analysis (CBA) will then be implemented by considering the CMS system reliability and costs varying with its failure detection effectiveness presented by the probabilistic detectability measure. Facilitated by CBA of the CMS, break-even points (BEP) between expected lifecycle benefits and the required CMS detectability level can be found to select optimal CMS for different system failure modes. Moreover, with the help of the CBA results, optimal maintenance strategies can be determined to minimize the O&M costs. The presented CBA methodology for the CMS systems will be demonstrated with an aircraft maintenance case study and the efficacy will be validated. === Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering