AI-AUGMENTED FMEA WITH ANTICIPATORY FAILURE REASONING

Stelian BRAD, Diana TICUDEAN, Bogdan BALOG, Alexandru Cirlejan, Vasile-Dragoș BARTOŞ, Anca STAN, Ionuț Adrian CHIȘ

Abstract


This paper proposes a hybrid methodology for proactive failure analysis that integrates classical FMEA with Anticipatory Failure Determination (AFD) and Artificial Intelligence (AI)-based reasoning. The framework introduces a four-layer architecture comprising structured failure capture, hypothetical failure expansion, AI-supported simulation and reasoning, and iterative design adaptation. Applied to a last-mile autonomous delivery robot, the model successfully identified novel failure modes, validated their impact using causal graphs and reinforcement learning, and generated targeted design improvements. Quantitative validation shows increased RPN accuracy and a reduced rate of undetected failures compared to classical FMEA, supporting the framework’s contribution to resilient, evolution-ready design.

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