Ranjith Kumar Sreenilayam Raveendran1 , Michael H. Azarian,1 Nam-Ho Kim,2 and Michael G. Pecht1
1CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
2Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA
Fault diagnosis of localized defects in motor bearings using electrical current signal analysis has
previously been reported in the literature. An extension of this concept is the detection of faults in
mechanical components influencing the operation of an induction motor, such as a gearbox connected to
the motor, using electrical current.
This paper presents a dynamic model-based approach to quantify the severity of upstream mechanical equipment faults on electrical current-based fault diagnosis metrics, using a wind turbine system as an example. A lumped parameter-based dynamic model was used to develop the system of equations representing the rotational motion of the drive train components in a wind turbine. This mechanical model was coupled with a dynamic model for a doubly-fed induction generator to simulate the electrical current output under faulty operating conditions. The changes in the fault diagnosis metric under different levels of severity of the fault were investigated. The influence of faults in multiple gears of the gearbox on the electrical current signals was also investigated. This method can be advantageous compared with existing approaches involving vibration analysis because it is a non-invasive and remote monitoring approach, and may not even require additional instrumentation. This approach is particularly useful for wind turbine operators as well as designers seeking to implement a low-cost condition monitoring solution for gear fault detection in a wind turbine.