@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.}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011},
 doi={10.48550/arXiv.1201.0490}
}

@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
  doi={10.48550/arXiv.1603.04467}
}

@misc{2022_nasa_prog_models,
    author    = {Christopher Teubert and Matteo Corbetta and Chetan Kulkarni and Katelyn Jarvis and Matthew Daigle},
    title     = {Prognostics Models Python Package},
    month     = December,
    year      = 2022,
    version   = {1.4},
    url       = {https://github.com/nasa/prog_models}
    }

  @article{christ2018time,
  title={Time series feature extraction on basis of scalable hypothesis tests (tsfresh--a python package)},
  author={Christ, Maximilian and Braun, Nils and Neuffer, Julius and Kempa-Liehr, Andreas W},
  journal={Neurocomputing},
  volume={307},
  pages={72--77},
  year={2018},
  publisher={Elsevier},
  doi={10.1016/j.neucom.2018.03.067}
}

@article{JMLR:v21:20-091,
  author  = {Romain Tavenard and Johann Faouzi and Gilles Vandewiele and
             Felix Divo and Guillaume Androz and Chester Holtz and
             Marie Payne and Roman Yurchak and Marc Ru{\ss}wurm and
             Kushal Kolar and Eli Woods},
  title   = {Tslearn, A Machine Learning Toolkit for Time Series Data},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {118},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v21/20-091.html}
}

@article{van2022predictive,
  title={Predictive maintenance for industry 5.0: behavioural inquiries from a work system perspective},
  author={van Oudenhoven, Bas and Van de Calseyde, Philippe and Basten, Rob and Demerouti, Evangelia},
  journal={International Journal of Production Research},
  pages={1--20},
  year={2022},
  publisher={Taylor \& Francis},
  doi={10.1080/00207543.2022.2154403}
}

@article{khan2023changes,
  title={Changes and improvements in Industry 5.0: A strategic approach to overcome the challenges of Industry 4.0},
  author={Khan, Moin and Haleem, Abid and Javaid, Mohd},
  journal={Green Technologies and Sustainability},
  volume={1},
  number={2},
  pages={100020},
  year={2023},
  publisher={Elsevier},
  doi={10.1016/j.grets.2023.100020} 
}
