Leveraging Physics-Based Features for Accurate Prediction of Remaining Useful Life of Bearing

The accurate prediction of the Remaining Useful Life (RUL) of machinery components, especially bearings, is essential for optimizing maintenance strategies, improving system reliability, and enhancing operational efficiency in industrial applications. This study investigates the potential of physics-based features to overcome the limitations of traditional data driven features-based approaches. The data driven methods often face challenges such as poor adaptability to varying operational conditions and a lack of physical interpretability necessary for understanding degradation processes. By embedding domain-specific physical principles into machine learning models, physics-based features enhance the accuracy and robustness of RUL predictions, delivering reliable results across diverse scenarios. This research utilizes vibration signal data from the FEMTO bearing dataset to evaluate Long Short-Term Memory (LSTM) networks using physics-based features, comparing their performance to Support Vector Regression (SVR), Regression Trees (RT), and models based on statistical features. Results demonstrate that physics informed features using LSTM models outperform other approaches, excelling at capturing temporal degradation patterns and delivering robust, interpretable RUL predictions. It is observed that results of LSTM with the involvement of physical insights have achieved greater results when validated against recent studies based on the error measuring indicators, Root Means Square Error (RMSE) and Mean Absolute Error (MAE). This deduces that the excellence of integrating physical insights with LSTM enriches the predictive maintenance, prognostics and health management (PHM) practices.