Bearings Fault Detection via Physics-Informed Convolutional Neural Networks: An experimental application

Abstract

The reliability of rotating machinery is essential in industrial environments, where early fault detection can prevent significant losses. In this scenario, Condition-Based Maintenance (CBM) strategies benefit from combined signal processing and machine learning techniques. Although Deep Learning (DL) based models present good results in automatic fault classification, their exclusive dependence on data can generate inconsistent predictions and non-compliance with physics. To overcome this limitation, this work proposes an approach that uses physical principles based on Physics-Informed Deep Learning (PIDL) for fault classification in bearings, using vibration data obtained in an experimental bench. The dataset covers three operating conditions — healthy, light damage and heavy damage — with vibration signals captured by two sensors, one always healthy and the other with or without damage. The methodology involves the application of the Hilbert transform (HHT) for envelope analysis and defining amplitude thresholds that reflect the physical levels of degradation. These thresholds are incorporated into the learning process, guiding the classification and promoting greater interpretability and robustness. Initial results show that, in the multiclass classification problem, traditional DL outperformed PIDL, achieving a balanced accuracy of 97.3% compared to 84.78% for PIDL. However, in the binary classification scenario — distinguishing between healthy and unhealthy conditions — the PIDL model achieved performance comparable to DL's, with balanced accuracies of 95.17% and 95.71%, respectively. These findings highlight that incorporating physical constraints into DL models can enhance the robustness of predictions, particularly in simpler classification contexts.