Application of Kalman Filter to prognostic method for estimating the RUL of a bridge rectifier

Prediction of the Remaining Useful Lifetime (RUL) of the system brings down the maintenance cost, downtime and also helps to take corrective measures. This results in avoiding catastrophic events. In this Paper, The RUL prediction is done based on KalmanFilter approach with dynamic curve fitting. The desired state (which is synthetic data) of the system is estimated based on a second order Kalman Filter, where Newtonian Kinematic model is used in tracking the significant feature state of the system. The dynamic curve fitting is done based on Least Square Error Sense method. The dynamically fitted curve is extrapolated until the failure threshold is reached and subsequently RUL is estimated. The algorithm thus developed is validated by a single phase full wave bridge rectifier analogous to aircraft Transformer Rectifier Unit (TRU), to obtain the real time significant feature data. The experimental results are compared with those of developed algorithms and results show Kalman filter based algorithm is ~95% accurate.