Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

Abstract

In order to improve the accuracy of bearings fault diagnosis, one of the most crucial components of rotating machinery, a novel features extraction procedure incorporating an improved features dimensionality reduction method is proposed. In the first step, using the empirical mode decomposition method, the original statistical characteristics were calculated from intrinsic mode functions of the vibration signal. Due to information redundancy of the original statistical characteristics, this paper presents a novel features extraction method that combines K-means method and standard deviation to select the most sensitive characteristics. Furthermore, a modified features dimensionality reduction method is proposed, to realize the low-dimensional representations for high-dimensional feature space. Finally, the performance of the fault diagnosis model is evaluated by vibration signals with 12 bearing fault conditions, which are provided by Bearing Data Center of Case Western Reserve University. Experiment results show that the proposed fault diagnosis model can serve as an effective and adaptive bearing fault diagnosis system.