A Prognostic Framework for Rotating Machines Considering Multi-Component Fault Scenarios

The importance of preventing failures in rotating machines has led to extensive research on diagnosis and prognosis methods based on vibration data. To achieve a high generalizability for these solutions, they need to handle high discrepancies between the data used for developing the method and the data from the target machine, a lack of historical events from the target machine and multi-component fault scenarios. Currently, state-of-the-art research has successfully found solutions targeting the first two challenges. However, these almost exclusively focus on single-component fault scenarios, meaning it is unclear how a method should be constructed considering a multi-component system. Therefore, this study constructs a framework for multi-component fault scenarios called Rotating Machinery Prognostic Framework (RoMaP) that leverages the advancements in anomaly detection, fault diagnosis and RUL prediction based on vibration data. To evaluate the potential of RoMaP, an instance based on state-of-the-art research was implemented and compared to other methods based on alternative frameworks. The results show that the instance of RoMaP had the best performance across many scenarios expected in an industrial environment, suggesting that it is a suitable approach for monitoring the health of rotating machines.