Prognostics of Rolling Element Bearings based on Cyclostationarity-based Indicators and Kalman filter under Varying Load and Speed

Rolling element bearings (REBs) are key components of rotating machines but the estimation of their remaining useful life (RUL) is still very challenging. First fault detection should be achieved as early as possible and then the RUL should be estimated as accurately as possible. Both steps require dedicated Health Indicators (HIs) which might not be the same when looking towards detection or prognostics. A key property of REB signals is cyclostationarity, as the statistical properties of their vibration behavior vary periodically over time. This characteristic has been effectively exploited to construct HIs for anomaly detection, and fault diagnosis in the field of condition monitoring (CM) achieving high performance. Although a plethora of methodologies have been proposed for RUL estimation, they usually are restricted in cases where the load conditions are assumed steady, reducing significantly their applicability and implementation in industry. Therefore there is a need for methodologies that are able to estimate the RUL of REBs operating under variable and/or varying load and speed conditions. The goal of this paper is the exploration of the performance of different vibration based HIs for fault detection, diagnosis and prognosis, including both time-domain and-order domain features. A dedicated bearing prognostics test rig was used to perform accelerated life tests of a self-aligned bearing, operating under varying load and speed conditions. The speed ranges from 0 to 3000 rpm and the load varies from 0 to 12 kN. The measurements lasted for around 400 hours and the bearing has an outer race fault in the loading zone. Different signals have been acquired during the tests, including accelerations, temperature and strain signals. The results indicate that the cyclic spectral coherence-based indicator is more sensitive to the change of states (healthy or damaged) and thus better for fault detection, while the correlation-based indicator is more sensitive to fault development, and therefore more suitable for the RUL estimation of REBs. Finally, to estimate RUL, different estimators, i.e., the Extended Kalman filter (EKF) and the Adaptive Kernal Kalman filter (AKKF), are used for RUL estimation.