Real time thermal estimation of a Brushed DC Motor by a steady-state Kalman filter algorithm in multi-rate sampling scheme

In industrial motion control systems, a reliable thermal protection of dc motors is necessary in reducing motor failures and increasing its lifespan. In this paper, a thermal estimation algorithm for the Brushed DC (BDC) Motor was designed and developed. A steady-state Kalman Filter Algorithm is used to estimate the speed and current states of the BDC Motor dynamic system without incurring large computational bandwidth. Given the states of the dynamic system, a second-order thermal model is then utilized and discretisized for the thermal estimation. A fully autonomous system is designed so that the algorithm is capable of any thermal issue detection without any interference from the users. The proposed algorithm is evaluated by comparing the motor winding temperature derived from the actual motor case temperature using a thermocouple and estimated motor winding temperature. Result shows that the estimated motor winding temperature is comparable to the actual motor winding temperature and the proposed technique is reliable in protecting mechanical systems without additional cost of thermal sensors.