Application of model-based fault detection to a brushless DC motor

In comparison to classical DC motors, brushless DC motors are very reliable, Nevertheless, they can also fail, caused by, e.g., overheating or mechanical wear. This paper proposes a parameter estimation technique for fault detection on this type of motor. Simply by measuring the motor's input and output signals, its parameters can be estimated. This method is based on a mathematical model of the process. In the presented work, a square-wave motor is considered. An appropriate model is derived. To be able to implement the method also on low-cost microcontroller-based control units, only the power inverter supply voltage, DC current, and the motor's angular velocity have to be measured. The parameter estimation technique provides information about the electrical resistance and the back-EMF constant as well as about the mechanical parameters. Comparing the nominal with the computed parameters, faults can be detected. The approach might be applied to both end-of-line and online fault detection. Results for simulated data demonstrate the capabilities of the proposed procedure. Finally, a real-world application-an actuation system with a brushless DC motor mounted to a gearbox-is given.