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Approach for fault prognosis using recurrent neural network

Author

Listed:
  • Qianhui Wu

    (Tsinghua University)

  • Keqin Ding

    (China Special Equipment Inspection and Research Institute)

  • Biqing Huang

    (Tsinghua University)

Abstract

In general, fault prognosis research usually leads to the research of remaining useful life prediction and performance prediction (prediction of target feature), which can be regarded as a sequence learning problem. Considering the significant success achieved by the recurrent neural network in sequence learning problems such as precise timing, speech recognition, and so on, this paper proposes a novel approach for fault prognosis with the degradation sequence of equipment based on the recurrent neural network. Long short-term memory (LSTM) network is utilized due to its capability of learning long-term dependencies, which takes the concatenated feature and operation state indicator of the equipment as the input. Note that the indicator is a one-hot vector, and based on it, the remaining useful life can be estimated without any pre-defined threshold. The outputs of the LSTM networks are connected to a fully-connected layer to map the hidden state into the parameters of a Gaussian mixture model and a categorical distribution so that the predicted output sequence can be sampled from them. The performance of the proposed method is verified by the health monitoring data of aircraft turbofan engines. The result shows that the proposed approach is able to achieve significant performance whether in one-step prediction task, in long-term prediction task, or in remaining useful life prediction task.

Suggested Citation

  • Qianhui Wu & Keqin Ding & Biqing Huang, 2020. "Approach for fault prognosis using recurrent neural network," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1621-1633, October.
  • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1428-5
    DOI: 10.1007/s10845-018-1428-5
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    References listed on IDEAS

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    1. A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
    2. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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    Cited by:

    1. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    2. Yu Mo & Liang Li & Biqing Huang & Xiu Li, 2023. "Few-shot RUL estimation based on model-agnostic meta-learning," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2359-2372, June.
    3. Liu, Lu & Song, Xiao & Zhou, Zhetao, 2022. "Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. 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.
    5. Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
    6. Galina Samigulina & Zarina Samigulina, 2022. "Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1433-1450, June.
    7. Shanmugasivam Pillai & Prahlad Vadakkepat, 2022. "Deep learning for machine health prognostics using Kernel-based feature transformation," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1665-1680, August.

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