Contextual Fault Diagnosis in Bearings Under Variable Operational Conditions Using Causal Disentanglement and Explainable Artificial Intelligence

Diagnosing rolling-element bearing faults under time-varying operating regimes remains challenging for predictive maintenance. We present an Explainable Causal Disentanglement Network (XCDN) that combines a Conv1D autoencoder with an explicit split of the latent space into causal (fault-related) and contextual (speed/RPM) factors, enforced via domain-adversarial training with a gradient-reversal layer. A grouped, stratified split prevents leakage; a small random search over hyperparameters selects the best setting, which is re-trained on train+validation and evaluated once on a held-out test set. On a variable-speed bearing dataset, XCDN attains test accuracy=0.9994 and macroF1=0.9994 (2 errors over 3,510 windows), with a confusion matrix separating Healthy, Inner Race Fault, and Outer Race Fault. Latent-space analysis indicates that the causal subspace zc forms three separable class clusters, whereas the domain subspace zd aligns smoothly with RPM and shows no class structure. Explainability audits corroborate these findings: Integrated Gradients and SHAP on zc identify a small subset of latent dimensions as the main drivers of each diagnosis, while input-level IG and a simple channel-ablation check indicate that vibration predominates in fault classification and RPM in the context regressor.The approach yields a compact, traceable representation that supports simple thresholding on a few latent nodes under time-varying conditions.