An improved FMEA model considering information quality in a multi-granularity probability linguistic environment

Abstract

As a significant analytical tool in reliability management, FMEA has been extensively used in various fields. Nevertheless, conventional FMEA has been criticized for some defects. To compensate this situation, this article proposes an improved FMEA method under the environment of probabilistic linguistic terms. The multiformity and indeterminacy of experts’ assessment information is depicted by applying probabilistic linguistic term sets, and then evaluation information is fused based on information quality and Dempster-Shafer evidence theory. The different action priority is adopted to determine the sequence of failure modes. Finally, a case study is presented to verify the applicability of the proposed method.

Keywords:

• D-S evidence theory
• failure modes and effects analysis (FEMA)
• information quality, PLTSs

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 72072089, 71931006, 71702072), the Qing Lan Project, and the China Postdoctoral Science Foundation (No. 2019T120429).

Notes on contributors

Linhan Ouyang

Linhan Ouyang is an associate professor in the College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. He received the BS degree in Industrial Engineering from Nanchang University, China, and the PhD degree in Management Science and Engineering from Nanjing University of Science and Technology, China. His research interests include quality engineering and quality management.

Yanhong Nie

Yanhong Nie is pursuing the MS degree in Industrial Engineering at the College of Economics and Management, Nanjing University of Aeronautics and Astronautics, China. She received the BS degree in Industrial Engineering from Nanjing University of Aeronautics and Astronautics, China. Her research interests include quality engineering and quality management.