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Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network

Author

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  • Feiyue Deng

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Yan Bi

    (School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Yongqiang Liu

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Shaopu Yang

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

Abstract

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes to construct the network framework. There are two main shortcomings of this approach: (1) the convolution operation based on multiple size convolution kernels requires enormous computation and has a low operational efficiency, which severely restricts its application in practical engineering. (2) The convolutional layer with a large size convolution kernel needs a mass of weight parameters, leading to a dramatic increase in the network training time and making it prone to overfitting in the case of small datasets. To address the above issues, a multi-scale dilated convolution network (MsDCN) is proposed for RUL prediction in this article. The MsDCN adopts a new multi-scale dilation convolution fusion unit (MsDCFU), in which the multi-scale network framework is composed of convolution operations with different dilated factors. This effectively expands the range of receptive field (RF) for the convolution kernel without an additional computational burden. Moreover, the MsDCFU employs the depthwise separable convolution (DSC) to further improve the operational efficiency of the prognostics model. Finally, the proposed method was validated with the accelerated degradation test data of rolling element bearings (REBs). The experimental results demonstrate that the proposed MSDCN has a higher RUL prediction accuracy compared to some typical CNNs and better operational efficiency than the existing multi-scale CNNs based on different convolution kernel sizes.

Suggested Citation

  • Feiyue Deng & Yan Bi & Yongqiang Liu & Shaopu Yang, 2021. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network," Mathematics, MDPI, vol. 9(23), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3035-:d:688685
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    References listed on IDEAS

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    1. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    2. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
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    Cited by:

    1. Zhengyang Fan & Wanru Li & Kuo-Chu Chang, 2023. "A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
    2. Yuanhong Mao & Zhong Ma & Xi Liu & Pengchao He & Bo Chai, 2023. "A Long-Term Prediction Method of Computer Parameter Degradation Based on Curriculum Learning and Transfer Learning," Mathematics, MDPI, vol. 11(14), pages 1-15, July.

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