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A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems

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  • Xu, Danyang
  • Qiu, Haobo
  • Gao, Liang
  • Yang, Zan
  • Wang, Dapeng

Abstract

Remaining useful life (RUL) estimation plays a crucial role in evaluating health states and improving maintenance plans of mechanical systems. Recently, artificial intelligence-based data-driven methods that use monitoring data as input have made significant progress in machine prognostics. However, current methods commonly ignore the correlations and internal differences of monitoring data, consequently leading to limited estimation performance. Therefore, this paper proposes a novel data-driven RUL estimation method named Dual-Stream Self-Attention Neural Network (DS-SANN). First, the multi-head self-attention mechanism is employed to learn correlations between different monitoring data and weigh the features dynamically to obtain global degraded information. Then, a dual-stream structure network is established to extract features from the original and auxiliary data simultaneously to make a comprehensive reflection of health states. The original and auxiliary data represent absolute values and internal differences of monitoring data, respectively. Finally, the multilayer perceptron is adopted to fuse the obtained features and estimate RUL. In addition, the effectiveness of DS-SANN is validated by the public degradation dataset of turbine engines. Compared with several existing prognostics methods, DS-SANN shows better estimation performance when averaging across all sub-datasets. Specifically, estimation effects evaluated by RMSE and Score improve 21.77% and 32.67%, respectively.

Suggested Citation

  • Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022001090
    DOI: 10.1016/j.ress.2022.108444
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    References listed on IDEAS

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    1. Zang, Yu & Shangguan, Wei & Cai, Baigen & Wang, Huasheng & Pecht, Michael. G., 2021. "Hybrid remaining useful life prediction method. A case study on railway D-cables," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Shi, Zunya & Chehade, Abdallah, 2021. "A dual-LSTM framework combining change point detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. 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.
    4. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    6. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2018. "Nonlinear step-stress accelerated degradation modelling considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 207-215.
    7. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. 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.
    9. Yu, Wennian & Tu, Wenbing & Kim, Il Yong & Mechefske, Chris, 2021. "A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    10. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    11. Liu, Junqiang & Lei, Fan & Pan, Chunlu & Hu, Dongbin & Zuo, Hongfu, 2021. "Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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    6. Zhuang, Liangliang & Xu, Ancha & Wang, Xiao-Lin, 2023. "A prognostic driven predictive maintenance framework based on Bayesian deep learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).

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