Adaptive feature extraction and SVM classification for real-time fault diagnosis of drivetrain gearboxes

Drivetrain gearboxes play an important role in many modern industrial applications. This paper presents a novel method consisting of adaptive feature extraction and support vector machine (SVM)-based classification for condition monitoring and fault diagnosis of drivetrain gearboxes operating in variable-speed conditions. An adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm are integrated in the proposed method for effective extraction of the features of gearbox faults from the stator current signal of the AC electric machine connected to the gearbox. A radial basis function kernel-SVM classifier is designed to identify the fault in the gearbox according to the fault features extracted. Experimental studies are performed for a drivetrain gearbox with a gear crack fault connected with a permanent magnet synchronous machine. Results show that the fault can be effectively identified by the proposed method.