Intelligent monitoring method of bearings based on PCA-SOM

Xianghui Xiao; Yanting Dai; Baixin Yin; Xiaofang Yuan

doi:10.1117/12.3055467

12 December 2024 Intelligent monitoring method of bearings based on PCA-SOM

Xianghui Xiao, Yanting Dai, Baixin Yin, Xiaofang Yuan

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134391G (2024) https://doi.org/10.1117/12.3055467
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

To address the issues of excessive maintenance and untimely maintenance of bearings, this paper proposes a performance evaluation method for bearing condition monitoring based on the combination of Principal Component Analysis (PCA) and Self-Organizing Map (SOM) networks. This method utilizes PCA to reduce the dimensionality of dual-domain features and conducts clustering analysis using the SOM network, constructing a minimal quantization error as a degradation indicator to assess the bearing degradation state. This is an unsupervised approach that does not require the setting of training labels for the network. Experiments were conducted on the IMS dataset, and the results indicate that this method can effectively identify early bearing faults and reduce reliance on human expertise, thereby enhancing both overall efficiency and the intelligence level of assessments, ultimately achieving intelligent bearing fault recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xianghui Xiao, Yanting Dai, Baixin Yin, and Xiaofang Yuan "Intelligent monitoring method of bearings based on PCA-SOM", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134391G (12 December 2024); https://doi.org/10.1117/12.3055467

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