1. Abid K (2020) Data-driven approach for fault prognostics of industrial systems-from using no, insufficient, to multiple historical degradation sequences. PhD thesis, Ecole nationale supérieure Mines-Télécom Lille Douai
2. Ali JB, Chebel-Morello B, Saidi L, Malinowski S, Fnaiech F (2015) Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mech Syst Signal Process 56:150–172
3. Andreolli I (2016) Introdução à elevação e escoamento monofásico e multifásico de petróleo. Interciência, Rio de Janeiro
4. Appana DK, Islam MR, Kim J-M (2017) Reliable fault diagnosis of bearings using distance and density similarity on an enhanced k-nn. In: Artificial life and computational intelligence: third Australasian conference, ACALCI 2017, Geelong, VIC, Australia, January 31–February 2, 2017, Proceedings 3. Springer, pp 193–203
5. Chammas A, Traore M, Duviella E, Sayed-Mouchaweh M, Lecoeuche S (2015) Drift detection and characterization for condition monitoring: application to dynamical systems with unknown failure modes. IMA J Manag Math 26(2):225–243