Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

Abstract

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.

Keywords

• data-driven prognostics
• motor prognostics