@article{susto2014machine,
  title={Machine learning for predictive maintenance: A multiple classifier approach},
  author={Susto, Gian Antonio and Schirru, Andrea and Pampuri, Simone and McLoone, Se{\'a}n and Beghi, Alessandro},
  journal={IEEE Transactions on Industrial Informatics},
  volume={11},
  number={3},
  pages={812--820},
  year={2014},
  publisher={IEEE},
  doi={10.1109/tii.2014.2349359}
}


@inproceedings{heimes2008recurrent,
  title={Recurrent neural networks for remaining useful life estimation},
  author={Heimes, Felix O},
  booktitle={2008 international conference on prognostics and health management},
  pages={1--6},
  year={2008},
  organization={IEEE},
  doi={10.1109/phm.2008.4711422}
}



@article{li2018remaining,
  title={Remaining useful life estimation in prognostics using deep convolution neural networks},
  author={Li, Xiang and Ding, Qian and Sun, Jian-Qiao},
  journal={Reliability Engineering \& System Safety},
  volume={172},
  pages={1--11},
  year={2018},
  publisher={Elsevier},
  doi={10.1016/j.ress.2017.11.021}
}

  @incollection{damant2021exploring,
  title={Exploring the Transition from Preventive Maintenance to Predictive Maintenance Within ERP Systems by Utilising Digital Twins},
  author={Damant, Liam and Forsyth, Amy and Farcas, Ramona and Voigtl{\"a}nder, Melvin and Singh, Sumit and Fan, Ip-Shing and Shehab, Essam},
  booktitle={Transdisciplinary Engineering for Resilience: Responding to System Disruptions},
  pages={171--180},
  year={2021},
  publisher={IOS Press},
  doi={10.3233/atde210095}
}



@article{serradilla2022deep,
  title={Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects},
  author={Serradilla, Oscar and Zugasti, Ekhi and Rodriguez, Jon and Zurutuza, Urko},
  journal={Applied Intelligence},
  pages={1--31},
  year={2022},
  publisher={Springer},
  doi={10.1007/s10489-021-03004-y}
}

@inproceedings{rastegari2015cost,
  title={Cost effectiveness of condition based maintenance in manufacturing},
  author={Rastegari, Ali and Bengtsson, Marcus},
  booktitle={2015 Annual Reliability and Maintainability Symposium (RAMS)},
  pages={1--6},
  year={2015},
  organization={IEEE},
  doi={10.1109/rams.2015.7105079}
}

@inproceedings{lemhadri2021lassonet,
  title={Lassonet: Neural networks with feature sparsity},
  author={Lemhadri, Ismael and Ruan, Feng and Tibshirani, Rob},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={10--18},
  year={2021},
  organization={PMLR},
  doi={10.48550/arXiv.1907.12207}
}

@article{fauvel2021xcm,
  title={Xcm: An explainable convolutional neural network for multivariate time series classification},
  author={Fauvel, Kevin and Lin, Tao and Masson, V{\'e}ronique and Fromont, {\'E}lisa and Termier, Alexandre},
  journal={Mathematics},
  volume={9},
  number={23},
  pages={3137},
  year={2021},
  publisher={MDPI},
  doi={10.3390/math9233137}
}

@inproceedings{jayasinghe2019temporal,
  title={Temporal convolutional memory networks for remaining useful life estimation of industrial machinery},
  author={Jayasinghe, Lahiru and Samarasinghe, Tharaka and Yuenv, Chau and Low, Jenny Chen Ni and Ge, Shuzhi Sam},
  booktitle={2019 IEEE International Conference on Industrial Technology (ICIT)},
  pages={915--920},
  year={2019},
  organization={IEEE},
  doi={10.1109/icit.2019.8754956}
}

@article{li2020remaining,
  title={Remaining useful life prediction using multi-scale deep convolutional neural network},
  author={Li, Han and Zhao, Wei and Zhang, Yuxi and Zio, Enrico},
  journal={Applied Soft Computing},
  volume={89},
  pages={106113},
  year={2020},
  publisher={Elsevier},
  doi={10.1016/j.asoc.2020.106113}
}

@article{CHEN2022104969,
title = {MSWR-LRCN: A new deep learning approach to remaining useful life estimation of bearings},
journal = {Control Engineering Practice},
volume = {118},
pages = {104969},
year = {2022},
issn = {0967-0661},
doi = {10.1016/j.conengprac.2021.104969},
url = {https://www.sciencedirect.com/science/article/pii/S096706612100246X},
author = {Yongyi Chen and Dan Zhang and Wen-an Zhang},
keywords = {Rolling bearing, Remaining useful life estimation, Deep learning, Long-term recurrent convolutional network, Attentional mechanism},
abstract = {Rolling bearings are important components of industrial rotating machinery and equipment. The prediction of the remaining useful life (RUL) of rolling bearings is of great significance for improving the safety of the machine, reducing the economic and property losses caused by the failure of the bearings. However, for the task of predicting the RUL of rolling bearings, the information of the past time and the future time are as important as the information of the current time. In order to make better use of the extracted features for RUL prediction of rolling bearings, this paper has proposed a novel deep learning framework of multi-scale long-term recurrent convolutional network with wide first layer kernels and residual shrinkage building unit (MSWR-LRCN). The major difference from the previous deep neural network is that our new network organically combines the attention mechanism with multi-scale feature fusion strategy, and improves the anti-noise ability of the entire network. In addition, moving average (MA) method and a polynomial fitting model are also used, which help predict the RUL of rolling bearings effectively. The results show that this method has improved the prediction accuracy compared with the existing methods.}
}