B. Iskandar

Fault Signature Maps: A Signal-Level Explainable Artificial Intelligence Framework for Bearing Fault Diagnosis

This study establishes that fault discrimination relies on transient impulse morphology rather than bearing characteristic frequencies, a finding invisible to feature-level XAI, and introduces a multi-resolution diagnostic framework bridging deep learning accuracy with physically interpretable vibration analysis for trustworthy deployment in safety-critical industrial environments.