Noise reduction in feature level and its application in rolling element bearing fault diagnosis

Vibration analysis is an effective way to accurately diagnose bearing faults, because it carries abundant information regarding mechanical health conditions. However, noise interference makes the features, extracted from vibration signals at different time periods, show randomness fluctuation that will reduce the bearing diagnostic accuracy. To solve this problem, this article proposes a noise reduction method in feature level and tries to use it in bearing fault diagnosis with principal component analysis and radial basis function neural network. First, original feature space, including time, frequency, and energy features, is constructed from these obtained vibration signals. Second, compendious feature sets of the considered bearing faults are created by principal component analysis and random statistical average algorithm. In this step, random statistical average is designed to weaken the influence of noise to features and principal component analysis is used to reduce the dimension of features for compendious feature sets. Then, radial basis function neural network, an artificial intelligence tool, is introduced to diagnose bearing faults by compendious feature sets. Finally, experiments on test bench are carried out to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify bearing faults.

Abstract licence: CC BY

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