Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS

Abstract

Failure mode and effects analysis (FMEA) is a widely used technique for identifying, evaluating, and eliminating potential failures in production, system, and process. The traditional FMEA ranks the failure modes according to risk priority numbers (RPN), which are obtained by the multiplications of the crisp values of risk factors, such as occurrence (O), severity (S), and detection (D). However, the traditional FMEA is criticized for mishandling uncertain information and calculating RPN unreasonably. To overcome the above deficiencies, this study presents an advanced FMEA method combined with interval 2-tuple linguistic variables (ITLV) and technique for order preference by similarity to ideal solution (TOPSIS). In the proposed method, the evaluations given by different FMEA members based on their different linguistic term sets are represented by ITLVs, which are feasible and valid variables to effectively deal with uncertain information. The TOPSIS method is used to rank the risk priorities of failure modes by comprehensively considering all of risk factors. Finally, an application case is provided to illustrate the validity and robustness of the proposed method.

Keywords:

• failure modes and effect analysis
• interval 2-tuple linguistic variables
• risk evaluation
• technique for order preference by similarity to ideal solution

Additional information

Funding

The work was supported by National Science and Technology Major Project of the Ministry of Science and Technology of China [2017ZX04002001]; Error! Hyperlink reference not valid. [51975249 and 51675227]; Industry Innovation Project of Jilin Province [2019C037-1]; and Jilin Scientific and Technological Development Program of China [20190101015JH]. The work was supported by program for JLU Science and Technology Innovative Research Team (JLUSTIRT); and Graduate Innovation Fund of Jilin University.

Notes on contributors

Guo-Fa Li

Guo-Fa Li received a PhD degree in mechanical engineering from Jilin University Changchun, China (2002). He is a professor in the School of Mechanical and Aerospace Engineering, Jilin University. His research interests include kinetic analysis of high-speed press, load spectrum, and reliability modeling of CNC machine tools.

Yi Li

Yi Li is a graduate student in the School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China. His research interest is accelerated life testing of CNC equipment.

Chuan-Hai Chen

Chuan-Hai Chen received a PhD in Mechanical Engineering from Jilin University, Changchun, China. He is an associate professor in the School of Mechanical and Aerospace Engineering, Jilin University, China. He focuses on load spectrum and failure analysis of machine tools.

Jia-Long He

Jia-Long He received a PhD in Mechanical Engineering from Jilin University, Changchun, China. He is a Lecturer in the School of Mechanical Science and Engineering, Jilin University. He focuses on load spectrum, reliability technology of CNC equipment, and industrial big data and intelligent manufacturing.

Tian-Wei Hou

Tian-Wei Hou is a graduate student in the School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China. His research interests are failure prediction and health management of tool magazine.

Jing-Hao Chen

Jing-Hao Chen is a graduate student in the School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China. His research interest is ransportation reliability of CNC equipment.