A probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory

As a means of identifying, preventing, and controlling potential system failures, failure mode and effect analysis (FMEA) has been extensively applied in many industries. However, the classical FMEA method has several defects in actual situations, such as being unable to express uncertain information, overlooking the weights of FMEA experts and risk factors (RFs), and without taking into account the experts’ psychological behaviors. To break through these constraints, a novel FMEA model based on probabilistic uncertain linguistic term sets (PULTSs), Organísation, rangement et Synthèse de données relarionnelles (in French) (ORESTE) and regret theory (RT) is proposed in this paper. First, PULTSs are introduced to express FMEA experts’ assessments under uncertainty. Then, the maximizing consensus model with PULTSs context (PUL-MCM) is constructed to obtain the weights of FMEA experts. Moreover, direct assessment weighting and maximizing deviation method (MDM) are utilized to determine the subjective and objective weights of RFs respectively, so as to reflect the importance of RFs comprehensively. Afterward, the extended ORESTE and RT are combined to prioritize FMs, which not only considers the psychological behaviors of experts and the detailed relations between failure modes (FMs) but avoids the information loss caused by the defuzzification of subjective weights of RFs. Finally, an example of the pasting process failures in lead-acid battery production is presented to illustrate the feasibility and rationality of the novel FMEA model, while sensitivity and comparative analyses are also performed to further demonstrate the flexibility and superiority of the novel FMEA model.