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Remaining useful life prediction

CNN

  • M. Zhao, S. Zhong, X. Fu, B. Tang, S. Dong and M. Pecht, "Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis," in IEEE Transactions on Industrial Electronics, vol. 68, no. 3, pp. 2587-2597, March 2021, doi: 10.1109/TIE.2020.2972458.link
  • Wang, B., et al., Multi-Scale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery. IEEE transactions on industrial electronics (1982), 2020: p. 1-1.link
  • Cheng, C., et al., A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings. IEEE/ASME Transactions on Mechatronics, 2020. 25(3): p. 1243-1254.link
  • Wang, B., et al., Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 2019. 134: p. 106330.link
  • Wang, B., et al., Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2019.link
  • Yang, B., R. Liu and E. Zio, Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture. IEEE Transactions on Industrial Electronics, 2019. 66(12): p. 9521-9530.link
  • Yu, W., I.Y. Kim and C. Mechefske, Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mechanical Systems and Signal Processing, 2019. 129: p. 764-780.link

LSTM

  • Chen, Z., et al., Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach. IEEE transactions on industrial electronics (1982), 2021. 68(3): p. 2521-2531.link
  • Yan, H., et al., Long-term gear life prediction based on ordered neurons LSTM neural networks. Measurement, 2020. 165: p. 108205.link
  • Ding, N., et al., Journal bearing seizure degradation assessment and remaining useful life prediction based on long short-term memory neural network. Measurement, 2020: p. 108215.link
  • Lyu, Y., et al., Joint Model for Residual Life Estimation Based on Long-Short Term Memory Network. Neurocomputing, 2020.link
  • Xiang, S., et al., LSTM networks based on attention ordered neurons for gear remaining life prediction. ISA Transactions, 2020.link
  • Sayah, M., et al., Robustness testing framework for RUL prediction Deep LSTM networks. ISA Transactions, 2020.link

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