Evidential FMEA method for human reliability assessment

Abstract

Evidence theory is a useful tool for modeling and reasoning uncertain information inherent in experts’ evaluations, which is not handled efficiently in traditional failure mode and effects analysis (FMEA). This study proposes an integrated FMEA method that incorporates evidence theory and is applied to human reliability assessment. The human error information of a human-machine system in FMEA is described as a directed graph by a Bayesian network (BN) to assess the dependence among potential human-related failure modes. The BN is extended to propagate the epistemic uncertainty of FMEA team members, where belief mass is applied to model uncertainties in team members’ knowledge and to convert their subjective cognition into varying levels of uncertainty. Risk indexes for occurrence, severity and detection from multiple sources are defined as a special assessment state. The combination of the belief mass of different failure modes is performed using extended Dempster’s rules to avoid the influence of conflicting evidence. Finally, an application in the healthcare system is provided to verify the effectiveness of our model. A comparison with other fuzzy FMEA methods is also conducted, demonstrating the advantages of the proposed model in dealing with decision-makers’ epistemic uncertainty and potential failure mode interdependencies.

Keywords:

• human reliability assessment
• failure mode and effects analysis (FMEA)
• evidence theory
• imprecise probability
• epistemic uncertainty

Disclosure statement

The authors declare that they have no conflicts of interest.

Ethical statement

Articles do not rely on clinical trials.

Human and animal participants

The submitted manuscript contains research that does not involve human participants and/or animal experimentation.

Additional information

Funding

This work was funded by the National Natural Science Foundation of China (NSFC) (71871121), Future Network Scientific Research Fund Project (FNSRFP-2021-YB-19), and Soft Science Project of China Meteorological Administration (2022zzxm24).

Notes on contributors

Mei Cai

Mei Cai was born in 1980, received the Bachelor of Management degree from Southeast University, Nanjing, China, in 2002; the Master of Management degree from Southeast University, Nanjing, China, in 2005; and Ph.D. from Southeast University, Nanjing, China, in 2012. Her current research interests include decision making under uncertainty, computing with words, and reasoning with uncertainty. She is currently full professor at Nanjing University of Information Science and Technology.

Jingmei Xiao

Jingmei Xiao was born in 1989. She is a PhD. student in the school of Management Science and Engineering, Nanjing University of Information Science and Technology. Her research interest is fuzzy decision analysis.

Qian Luo

Qian Luo as born in 1980, received Ph.D. from Southeast University, Nanjing, China, in 2014. Her current research interests include business management. She is currently lecture at Nanjing University of Information Science and Technology.

Yu Gao

Yu Gao was born in 1998. She is a M.S. student in the school of Management Science and Engineering, Nanjing University of Information Science and Technology. Her research interest is fuzzy decision analysis.

Xinglian Jian

Xinglian Jian was born in 1998. She is a M.S. student in the school of Management Science and Engineering, Nanjing University of Information Science and Technology. Her research interest is fuzzy decision analysis.

Ya Wang

Ya Wang was born in 1998. She is an M.S. student in the School of Management Science and Engineering, Nanjing University of Information Science and Technology. Her research interest is fuzzy decision analysis.