Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
Failure modes and effects analysis (FMEA) is a very useful reliability-management instrument for detecting and mitigating risks in various fields. Linguistic assessment approach has recently been widely used in FMEA. Words mean different things to different people, so FMEA members may present personalized individual semantics (PIS) in their linguistic assessment information. This paper designs a PIS-based FMEA approach with members expressing their opinions over failure modes and risk factors using linguistic distribution assessment matrices (LDAMs) and also provide their opinions over failure modes using incomplete additive preference relations (APRs). A preference information preprocessing method with a two-stage optimization model is presented to generate complete APRs with acceptable consistency levels from incomplete APRs. Then, a deviation minimum-based optimization model is designed to personalize individual semantics by minimizing the deviation between APR and the numerical assessment matrix derived from the corresponding LDAM. This is followed by the developing of a ranking process to generate the risk ordering of failure modes. A case study and a detailed comparison analysis are presented to show the effectiveness of the PIS-based linguistic FMEA approach.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Zhang, H., Xiao, J., Dong, Y., Chiclana, F. and Herrera-Viedma, E. (2020) Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information. IISE Transactions,
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes