Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning

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Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.