New dynamic reliability assessment method based on process capability index and fault importance index

Abstract

Accurate and effective equipment state assessment can help us to keep abreast of equipment operation reliability in performing emergency management of equipment faults. However, the state change of a running system is a dynamic process, and the occurrences of faults are a random process. Therefore, the dynamic characteristics of a system should be identified and real-time data should be used to establish a state evaluation model. This paper proposes a dynamic reliability assessment method of equipment based on process capability index (PCI) and fault importance index (FII) to identify the dynamic performance of equipment, evaluate the stability of process data, and analyze the structural importance of the functional components in the equipment. We present a fault importance calculation method and a comprehensive PCI (CPCI) method based on structural importance. These methods are applied to a synchrotron cooling water system in Shanghai Proton and Heavy Ion Center. Result shows that the dynamic reliability assessment model based on PCI and FII can effectively identify the equipment dynamic reliability change.

Keywords:

• C_pk
• fault diagnosis
• fault importance index
• process capability index
• state assessment

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant nos. 61672338 and 61373028, and Shanghai Municipal Committee of Science and Technology under Grant nos.16511102500.

Notes on contributors

Guangzhong Liu

Guangzhong Liu received the PhD degree from the China University of Mining and Technology. He is currently a Professor of computer science and engineering at Shanghai Maritime University. His specific research interests include underwater acoustic communication technology, mobile networking, wireless communication, network security and systems reliability analysis. E-mail: [email protected]

Wanhong Li

Wanhong Li is currently a PhD Candidate in Shanghai Maritime University. He received the MS degree from the East China University of Science and Technology. His specific research interests include intelligent information processing and systems reliability analysis. E-mail: [email protected]