Classification of Bearing Faults Combining Compressive Sampling, Laplacian Score, and Support Vector Machine

Rolling element bearings have a pivotal role in rotating machine and their failures are the leading cause of more substantial failures in the machine. In response to their importance, there is a growing body of research looking at condition monitoring of rolling element bearings to avoid machine breakdowns. In this study, by taking advantages of Compressive Sampling (CS), Laplacian Score (LS) and Multi-class Support Vector Machine (MSVM), an intelligent method for rolling bearing fault classification is proposed The CS is used to obtain compressed samples of the raw vibration signals, and the LS is used to rank the features of the obtained compressed samples with respect to their importance and correlations with the core fault characteristics. Then, based on LS ranking, we selected a small amount of the most significant compressed samples to produce the features vector. Finally, classification performance using MSVM shows high classification accuracy with a significantly reduced feature set.