Electric motors are widely used in recent industry from manufacturing to aerial vehicles to operate and control of power system, hence, its health management is gaining more importance. To meet this demand, a Kalman filter-based framework is developed for the PHM of DC motors by exploiting the motor dynamic model and control signals acquired during operation. The framework consists of an online health diagnosis to estimate the health status of the motor and each component, and an offline failure prognosis to predict the RULs. The approach is demonstrated by the data collected from accelerated degradation tests of satellite motor, in which the health is estimated at the current cycle, and RUL is predicted against failure thresholds.