Monitoring of rotating machinery with AI

Condition monitoring of rotating machinery, including fault detection, fault diagnosis and estimation of Remaining Useful Life (RUL), offers significant cost benefits to industry by minimizing unexpected downtimes and failures. Data-driven approaches, often based on Deep Learning, have achieved significant performance. However, limited data availability for model training, influence of varying operating conditions, lack of interpretability and need for robustness and reliability in predictions pose significant challenges in the application of AI based models in real-world applications.

The goal of this talk is to present a methodology for diagnostics and prognostics under varying operating conditions, based on Digital Twins and Transfer Learning, which mitigates the need for large historical data for model training, estimating and quantifying in parallel the epistemic and aleatoric uncertainty of predictions, addressing the safety issues in RUL prediction. Moreover, a domain transformation technique, which, in combination with existing gradient-based XAI algorithms, enables the explanation in a domain different from the input domain of the machine learning model, will be introduced. The methodologies will be applied to various use cases from rotating machinery, with emphasis on rolling element bearings, and their performance will be discussed.