Fault detection and classification of permanent magnet synchronous motor in variable load and speed conditions using order tracking and machine learning

Abstract

Permanent magnet synchronous machines have gained popularity in wind turbines due to their merits of high efficiency, power density, and reliability. The wind turbines normally work in a wide range of operations, and harsh environments, so unexpected faults may occur and result in productivity losses. The common faults in the permanent magnet machines occur in the bearing and stator winding, being mainly detected in steady-state operating conditions under constant loads and speeds. However, variable loads and speeds are typical operations in wind turbines and powertrain applications. Therefore, it is important to detect bearing and stator winding faults in variable speed and load conditions. This paper proposes an algorithm to diagnose multiple faults in variable speed and load conditions. The algorithm is based on tracking the frequency orders associated with faults from the normalised order spectrum. The normalised order spectrum is generated by resampling the measured vibration signal via estimated motor speeds. The fault features are then generated from the tracking orders in addition to the estimated torque and speed features. Finally, support vector machine algorithm is used to classify the faults. The proposed method is validated using experimental data, and the validated results confirm its usefulness for practical applications.