Abstract
In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time–frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD. Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by TJ, YL and SL. The first draft of the manuscript was written by TJ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Jiang, T., Li, Y. & Li, S. Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm. Soft Comput 27, 8175–8187 (2023). https://doi.org/10.1007/s00500-022-07704-6
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DOI: https://doi.org/10.1007/s00500-022-07704-6