Wavelet  machine learning model for condition monitoring of ball bearings

Abstract

This research presents an integrated condition monitoring framework for deep groove ball bearings by combining Complex Morlet Wavelet (CMW) analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools. The proposed methodology employs Fast Fourier Transform (FFT) and Complex Morlet Wavelet-based vibration signal processing to extract discriminative time–frequency features for the early detection and diagnosis of bearing faults under both single and combined fault conditions. To evaluate fault classification performance, three machine learning algorithms, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), are implemented and comparatively analyzed. Experimental investigations are conducted on a laboratory-scale bearing test rig operating under controlled conditions. The extracted wavelet-based features effectively characterize fault-induced vibration signatures, enabling accurate fault identification and classification. Comparative results indicate that Random Forest achieves the highest classification accuracy, followed by SVM and ANN. The average classification accuracies obtained using RF, SVM, and ANN are 97.48%, 95.27%, and 87.20%, respectively, demonstrating the superior robustness and generalization capability of the ensemble learning approach. Furthermore, thermographic analysis and SKF Machine Condition Advisor measurements provide complementary information for validating fault severity and machine health conditions, thereby enhancing diagnostic reliability. Although the present study is limited to constant-speed operation and controlled laboratory environments, the proposed framework demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications. The integration of advanced time–frequency analysis, machine learning-based fault classification, and practical condition monitoring tools offers an effective and reliable solution for machinery health assessment in industrial environments.