Reliability based lifetime prediction and optimization for permanent magnet synchronous machines

This paper presents a methodology for on-line evaluation of electrical motors current state, used to predict its life-time and to increase the residual lifetime. Based on the estimated motor state, the residual lifetime can be extended drastically by reducing the load. Also maintenance costs can be reduced. The current motor health state is observed based on the on-line calculation of the motor load. Residual lifetime is calculated using statistical data, providing the probability of failure if the system is kept running. Algorithms for optimized motor control are based on a clear decision concept. Here we introduce health rate (HR) as a new input for the controller. The HR value is computed using motor damage estimated from motor lifetime and the motor run time. According to HR value, the controller then adjusts control values such as current, velocity, angle, torque, etc. The methodology is demonstrated using simulation. The results show that a good compromise between motor performance and predicted residual lifetime can be found in order to increase the systems availability and moreover to operate faultlessly.