A Data-Driven Prognostics Technique and RUL Prediction of Rotating Machines Using an Exponential Degradation Model

Accurate Remaining Useful Life (RUL) prediction of machines is important for condition-based maintenance, in order to improve the reliability and costs of maintenance. The rotor is one of the most important equipment parts and is one of the most common failure points. To assess the degradation life of rotating machines, this paper proposes a data-driven prognostic technique that utilizes an unsupervised trend extraction and an exponential degradation model to obtain an accurate RUL prediction of rotor failures. The main steps of the proposed prognostic technique are condition monitoring data acquisition, feature extraction using signal processing techniques, feature selection based on the monotonicity technique to quantify the merit of the best representative features for prognosis purposes. The selected features are then combined using the principal component analysis technique to get the most appropriate component health indicator. Finally, an exponential degradation model is used to estimate the RUL using this health indicator. The suitability of the proposed approach in component monitoring is demonstrated on a degradation dataset of a faulty rotor acquired from a Simulink model of an induction motor.