Intelligent Roller Bearing Fault Diagnosis in Industrial Internet of Things

Abstract

Advanced research studies on industrial Internet of things require effective feature extraction and accurate machinery health state evaluation. For roller bearing, a well-known mechanical component most extensively used in the industry, its running status directly affects the operation of the entire machinery and equipment. For intelligent fault diagnosis of roller bearing, the selection of the intrinsic mode function (IMF) modes in approaches of ensemble empirical mode decomposition (EEMD)/variational mode decomposition (VMD) becomes a tricky problem. To solve this problem, this study proposed an efficient scheme on roller bearing fault diagnosis that combines the refined composite multivariate multiscale sample entropy (RCMMSE) with different classifiers. Firstly, the synthetic noise signals are introduced to compare the effectiveness of the multiscale sample entropy (MSE) and the RCMMSE models. Secondly, the random noise signals are used to compare the performance of EEMD and VMD methods, where the envelope spectrum characteristics of fault signals are well described. Moreover, EEMD/VMD methods are utilized to decompose the roller bearing vibration signals into various modes to get the entropy values. Finally, the obtained RCMMSE is adopted as a feature vector and subsequently employed as an input of support vector machine, random forest, and probabilistic neural network models to conduct roller bearing fault identification. The extensive experimental results prove that this proposed scheme performs well and the classification accuracy of VMD-RCMMSE is higher than EEMD-RCMMSE.