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

Abstract

Deep learning models for bearing fault diagnosis on the Case Western Reserve University (CWRU) dataset routinely achieve near-perfect accuracy. Yet their decisions remain largely opaque at the signal level, so engineers cannot determine where in the raw vibration waveform the model focuses. This study aims to bridge the interpretability gap in deep learning-based bearing fault diagnosis by developing a signal-level explainability framework for the Wide Deep Convolutional Neural Network (WDCNN) on the Case Western Reserve University (CWRU) dataset. SHAP DeepExplainer is applied directly to raw 2,048-point vibration segments to produce per-sample-point attribution maps; Fault Signature Maps (FSMs) are formalized as class-averaged SHAP fingerprints in three variants (signed, absolute, and variance) and validated via discriminability index, severity monotonicity, and split-half stability, complemented by a three-variant ablation study examining how architectural decisions affect both accuracy and explainability. On the 10-class CWRU dataset under a rigorous temporal split (1,240/310/750 samples), WDCNN achieves 99.87% test accuracy with a macro F1-score of 0.997. FSMs demonstrate high reproducibility (split-half stability = 0.940); severity monotonicity ranges from 8.6% to 17.6%; and ablation reveals that removing Batch Normalization increases FSM discriminability by 2.3× at a 3.6% accuracy cost. As the first work applying SHAP DeepExplainer at full 2,048-point raw signal resolution for WDCNN, 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.