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2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing

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  • Guan, Yang
  • Meng, Zong
  • Sun, Dengyun
  • Liu, Jingbo
  • Fan, Fengjie

Abstract

Rolling bearing is an indispensable element of rotating machinery, timely and accurate fault diagnosis of rolling bearing plays an important role in the safe and reliable operation of modern industrial systems. Considering the bottleneck that the information collected by a single sensor and single scale features extracted by conventional networks are not comprehensive, a multi-sensor and multi-scale model (2MNet) is proposed to bring a new perspective to accurate fault diagnosis. Most notably, multi-sensor vibration signals in three directions can be fused by defining a novel correlation kurtosis weighted fusion rule. Furthermore, the implication of multi-scale is twofold: one is the multi-scale feature extraction by optimizing the conventional deep residual network and adding dilated convolution, and the other is to achieve multi-scale feature fusion by combining the pyramid principle which can connect deep and shallow features. The superiority and applicability of the model are confirmed by numerical simulation and rolling bearing data.

Suggested Citation

  • Guan, Yang & Meng, Zong & Sun, Dengyun & Liu, Jingbo & Fan, Fengjie, 2021. "2MNet: Multi-sensor and multi-scale model toward accurate fault diagnosis of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005263
    DOI: 10.1016/j.ress.2021.108017
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    References listed on IDEAS

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    Cited by:

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    9. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    10. Zhuang, Jichao & Jia, Minping & Cao, Yudong & Zhao, Xiaoli, 2022. "Semi-supervised double attention guided assessment approach for remaining useful life of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
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    13. Wang, Huan & Li, Yan-Fu, 2023. "Bioinspired membrane learnable spiking neural network for autonomous vehicle sensors fault diagnosis under open environments," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    15. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    16. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    17. 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).
    18. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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