Advanced Diagnosis of Stator Faults in Large Induction Motors Based on Internal Flux Measurement

Electric motors used in large mining machines (electric shovels, draglines) undergo dynamic stresses at high power levels. Their lifetime is reduced as compared to constant speed motors, and downtime costs are very high. To monitor condition of motors in such applications, advanced sensor instrumentation is considered, including installation of Hall effect flux sensors (HEFS) inside the motor air gap. Such an advanced instrumentation takes condition monitoring of electric motors to a new level. It enables continuous measurements of the air gap flux in both time and space domains simultaneously. This allows for advanced fault detection and fault prediction algorithms. In this paper, a method for early diagnosis of stator turn-to-turn faults is presented. Using air gap flux measurements via an array of Hall Effect Flux Sensors, the proposed method detects, localizes and quantifies the stator faults with high accuracy and sensitivity. Furthermore, the fault can be detected at an incipient stage and its development can be predicted. The results of the presented study have been supported by both simulation and experimental results obtained on a laboratory scale induction motor.