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Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

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  • Quanbo Lu
  • Xinqi Shen
  • Xiujun Wang
  • Mei Li
  • Jia Li
  • Mengzhou Zhang

Abstract

Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

Suggested Citation

  • Quanbo Lu & Xinqi Shen & Xiujun Wang & Mei Li & Jia Li & Mengzhou Zhang, 2021. "Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:2530315
    DOI: 10.1155/2021/2530315
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    Cited by:

    1. Yan, Shen & Shao, Haidong & Min, Zhishan & Peng, Jiangji & Cai, Baoping & Liu, Bin, 2023. "FGDAE: A new machinery anomaly detection method towards complex operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Yan Ren & Linlin Zhang & Jiangtao Chen & Jinwei Liu & Pan Liu & Ruoyu Qiao & Xianhe Yao & Shangchen Hou & Xiaokai Li & Chunyong Cao & Hongping Chen, 2022. "Noise Reduction Study of Pressure Pulsation in Pumped Storage Units Based on Sparrow Optimization VMD Combined with SVD," Energies, MDPI, vol. 15(6), pages 1-18, March.
    3. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    4. Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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