A Bayesian FMEA-Based Method for Critical Fault Identification in Stacker-Automated Stereoscopic Warehouses

This study proposes a Bayesian failure mode and effects analysis (FMEA)-based method for identifying critical faults and guiding maintenance decisions in stacker-automated stereoscopic warehouses, addressing the limited research on whole-machine systems and the interactions among fault modes. First, the hesitant fuzzy evaluation method was utilized to assess the influences of risk factors and fault modes in a stacker-automated stereoscopic warehouse. A hesitant fuzzy design structure matrix (DSM) was then constructed to quantify their interaction strengths. Second, leveraging the interaction strengths and causal relationships between severity, detection, risk factors, and fault modes, a Bayesian network model was developed to compute the probabilities of fault modes under varying severity and detection levels. FMEA was subsequently applied to evaluate fault risks based on severity and detection scores. Following this, fault risk ranking was conducted to identify critical fault modes and formulate targeted maintenance strategies. The proposed method was validated through a case study of Company A’s stacker-automated stereoscopic warehouse. The results demonstrate that the proposed approach can more objectively identify critical fault modes and develop more precise maintenance strategies. Furthermore, the Bayesian FMEA method provides a more objective and accurate reflection of fault risk rankings.