Publisher
Springer Nature Switzerland
Reference27 articles.
1. Aivaliotis, P., Arkouli, Z., Georgoulias, K., Makris, S.: Degradation curves integration in physics-based models: towards the predictive maintenance of industrial robots. Robot. Comput.-Integr. Manuf. 71, 102177 (2021)
2. Bahador, A., et al.: Condition monitoring for predictive maintenance of machines and processes in ARTC model factory. Implementing Industry 4.0, pp .113–141 (2021)
3. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 137, 106024 (2019)
4. Cauchi, N., Macek, K., Abate, A.: Model-based predictive maintenance in building automation systems with user discomfort. Energy 138, 306–315 (2017)
5. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint. arXiv:1511.07289 (2015)