Explainable Deep Learning for Bearing Fault Diagnosis: Architectural Superiority of ResNet-1D Validated by SHAP

Rolling element bearing fault diagnosis (BFD) is fundamental to Predictive Maintenance (PdM) strategies for rotating machinery, as early anomaly detection prevents catastrophic failures, reduces unplanned downtime, and optimizes operational costs. This study introduces an interpretable Deep Learning (DL) framework that rigorously compares the performance of an Artificial Neural Network–Multilayer Perceptron (ANN-MLP), a one-dimensional Convolutional Neural Network (1D-CNN), and a ResNet-1D architecture for classifying seven bearing health states using a compact vector of 15 statistical features extracted from vibration signals. Both baseline models (ANN-MLP and 1D-CNN) failed to detect the critical Abrasive Particles fault (F1 = 0.0000). In contrast, the ResNet-1D architecture achieved statistically superior diagnostic performance, successfully resolving the most challenging class with a perfect F1-score of 1.0000 and an overall macro F1-score of 0.9913. This superiority was confirmed by a paired t-test on 100 bootstrap samples, establishing a highly significant difference in performance against the 1D-CNN (t=592.702, p=0.00000). To boost transparency and trust, the SHapley Additive exPlanations (SHAP) method was applied to interpret the ResNet-1D’s decisions. The SHAP analysis revealed that the Crest Factor from Sensor 1 (Crest_1) exerts the strongest influence on the critical Abrasive Particles fault predictions, physically validating the model’s intelligence against established domain knowledge of impulsive wear events. These findings support transparent, highly reliable, and evidence-based decision-making in industrial PdM applications within Industry 4.0 environments.