Diagnosis of Rolling Bearing Based on Classification for High Dimensional Unbalanced Data

Motor systems are becoming more and more vital in modern manufacturing and bearings play an important role in the performance of a motor system. Many problems that arise in motor operation are related to bearing faults. In many cases, the accuracy of the devices for monitoring or controlling a motor system highly depends on the dynamic properties of motor bearings. Thus, fault diagnosis of a motor system is inseparably related to the diagnosis of the bearing assembly. The fault diagnosis of rolling bearings is substantially a classification problem. The traditional application of random forest (RF) to fault diagnosis methods is based on balanced data. However, in a practical situation, it is difficult to collect the fault data that are usually unbalanced. In order to solve this problem, in the first step, we propose a two-step (TS) clustering algorithm to enhance the original synthetic minority oversampling technique (SMOTE) algorithm for the unbalanced data classification. Then, based on the improvement of the SMOTE algorithm, we propose the principal component analysis (PCA) and apply it in the field of high-dimensional unbalanced fault diagnosis data. In this paper, we apply this new method to the fault diagnosis of rolling bearings, and the experiments conducted in the end show that the improved algorithm has a better classification performance.