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Modelling long- and short-term multi-dimensional patterns in predictive maintenance with accumulative attention

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  • Shi, Yong
  • Zhang, Linzi

Abstract

Predictive Maintenance (PdM) plays a pivotal role in safety management by planning necessary maintenance in advance to avoid future serious breakdown. Predicting the Remaining useful life (RUL) based on historical running data is an important task in PdM. One challenge of this issue is to capture both the temporal and spatial complex patterns especially in ultra-long sequences. Recent studies have demonstrated the superiority of Transformer model in capturing long-term dependencies. However, in the research field of PdM, the canonical Transformer faces with difficulties to deploy due to its limited input length, neglect of local correlations, insensitivity to input pattern and high computational cost. To tackle this, a novel lightweight RUL prediction model called TCNASA integrating temporal convolution network (TCN), accumulative self-attention layer (ASA) and autoregressive component is proposed. It uses TCN firstly to capture local correlations, prunes the redundant short-term cases when matching pairs in attention layers, accumulates global patterns through stacked self-attention layers, and lastly integrates an autoregressive component to enhance the robustness. The experimental results on several real-world PdM datasets have verified the effectiveness and efficiency of the proposed TCNASA model.

Suggested Citation

  • Shi, Yong & Zhang, Linzi, 2023. "Modelling long- and short-term multi-dimensional patterns in predictive maintenance with accumulative attention," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s095183202300220x
    DOI: 10.1016/j.ress.2023.109306
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    References listed on IDEAS

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    1. Amrin, Andas & Zarikas, Vasileios & Spitas, Christos, 2018. "Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 211-225.
    2. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    3. Li, Xingyu & Krivtsov, Vasiliy & Arora, Karunesh, 2022. "Attention-based deep survival model for time series data," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    7. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
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