A Novel Fault Diagnosis Method Based on the KELM Optimized by Whale Optimization Algorithm

Abstract

To solve the problem that fault features are difficult to extract and the time-frequency features cannot fully represent the state information, a novel method is proposed in this paper based on the whale optimization algorithm (WOA) and the kernel extreme learning machine (KELM). First, the vibration signals are processed by the ensemble empirical mode decomposition and sample entropy to obtain the feature vectors. Based on this, a KELM model for fault diagnosis is established. Then, the penalty factor and the kernel parameters in the KELM are optimized by WOA to improve the stability and classification accuracy. Taking faults of a ball-screw pair on a linear feed table as a case, the experimental results indicate that the proposed method can effectively extract the fault features of the ball-screw pair, and it can achieve higher classification accuracy, faster convergence speed, and greater convergence precision than the existing fault diagnosis methods.