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