An Improved Fault Diagnosis Method for Rolling Bearing Based on Relief-F and Optimized Random Forests Algorithm

Rolling Bearings are important supporting components of rotating machines in industrial processes; the faults of rolling bearings will cause the deterioration of the operation conditions of rotating machines. How to effectively extract the fault features and identify the fault modes of rolling bearings quickly and accurately has become a key issue for the safe operation of rotating machines. In this paper, a novel hybrid fault diagnosis method of an optimized random forests classifier for rolling bearings is proposed. Firstly, the original vibration signals are decomposed by recursive variational mode decomposition (RVMD), and the typical time–frequency domain features are extracted from decomposed signals at different scales. The Relief-F ranking method is utilized to assess the quality of time–frequency domain features, and the top-ranked features with high weight gain are selected for identifying the fault modes. Then, an improved bee colony algorithm is proposed based on the simulated binary crossover criterion, which is used to optimize the key parameters of random forests (RF). Finally, several experiments are conducted on the Case Western Reserve University bearing dataset and the dataset collected from our rolling bearing fault testbed. The experimental results show that the proposed method can accurately identify bearing faults and outperform other state-of-the-art methods.