Fault identification of ball bearings using Fast Walsh Hadamard Transform, LASSO feature selection, and Random forest classifier
Vipul Dave, H. Thakker, Vinay Vakharia
Abstract
To reveal the machinery health condition, time-frequency analysis is an effective tool when signals are non-stationary. To identify bearing faults, numerous techniques have been proposed by various researchers. However, little research focused on image processing-based texture feature extraction for the identification of faults. The time-frequency image contains many sensitive fault information regarding bearing conditions, which can be extracted in the form of features. Therefore, in this paperwork, a methodology is proposed based on Fast Walsh Hadamard Transform (FWHT) time-frequency spectrogram, gray level co-occurrence matrix (GLCM), and machine learning techniques. A feature vector is constructed which consists of one dimension and two-dimension features extracted from Fast Walsh Hadamard Transform coefficients. To identify the fault conditions, LASSO-based feature ranking is applied to determine the suitable features. Finally, classifiers like Support vector machine (SVM), Random forest, and K-nearest neighbors (KNN) are evaluated for identifying bearing faults. Training, Testing, five-fold cross-validation performed on fusion feature vector. Results indicate that ranked fusion features are effective to diagnose bearing faults with good accuracy.