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Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

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  • Xihui Chen
  • Liping Peng
  • Gang Cheng
  • Chengming Luo

Abstract

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.

Suggested Citation

  • Xihui Chen & Liping Peng & Gang Cheng & Chengming Luo, 2019. "Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN," Complexity, Hindawi, vol. 2019, pages 1-12, March.
    • Handle: RePEc:hin:complx:8716979
    DOI: 10.1155/2019/8716979
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    References listed on IDEAS

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    1. Jian Ma & Hua Su & Wan-lin Zhao & Bin Liu, 2018. "Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning," Complexity, Hindawi, vol. 2018, pages 1-13, July.
    2. Wen, Tao & Jiang, Wen, 2018. "An information dimension of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 388-399.
    3. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    4. Hongmei Liu & Jiayao Jing & Jian Ma, 2018. "Fault Diagnosis of Electromechanical Actuator Based on VMD Multifractal Detrended Fluctuation Analysis and PNN," Complexity, Hindawi, vol. 2018, pages 1-11, August.
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    Cited by:

    1. Wei Jiang & Jianzhong Zhou & Yanhe Xu & Jie Liu & Yahui Shan, 2019. "Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model," Complexity, Hindawi, vol. 2019, pages 1-18, October.
    2. Yongbo Li & Xianzhi Wang & Shubin Si & Xiaoqiang Du, 2019. "A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography," Complexity, Hindawi, vol. 2019, pages 1-12, August.

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