**Author details**

Fausto Pedro García Márquez Ingenium Research Group, Universidad Castilla-La Mancha, Ciudad Real, Spain

\*Address all correspondence to: faustopedro.garcia@uclm.es

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**3**

2013

2011

*Introductory Chapter: Prognostics - An Overview DOI: http://dx.doi.org/10.5772/intechopen.86894*

> [10] Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D. A survey of artificial neural network in wind energy systems. Applied Energy.

> [11] Benmessaoud T, Marugán AP, Mohammedi K, Márquez FPG. Fuzzy logic applied to scada systems. In: International Conference on Management Science and Engineering

Management. Springer; 2017.

Learning. 1998;**32**:41-62

2013;**67**:969-981

[14] Quiñonero-Candela J,

Research. 2005;**6**:1939-1959

Rasmussen CE. A unifying view of sparse approximate gaussian process regression. Journal of Machine Learning

[15] Márquez FPG, Lev B. Big Data Management. Springer; 2017

[12] Fine S, Singer Y, Tishby N. The hierarchical hidden Markov model: Analysis and applications. Machine

[13] Manupati V, Anand R, Thakkar J, Benyoucef L, Garsia FP, Tiwari M. Adaptive production control system for a flexible manufacturing cell using support vector machine-based approach. The International Journal of Advanced Manufacturing Technology.

2018;**228**:1822-1836

pp. 749-757

[1] EN\_15341:2007. Maintenance— Maintenance Key Performance Indicators. Europeam Standard; 2010

Márquez FPG. Economic viability analytics for wind energy maintenance management. In: Advanced Business Analytics. Springer; 2015. pp. 39-54

[3] Jiménez AA, Muñoz CQG, Márquez FPG. Machine learning and neural network for maintenance management. In: International

[4] Marugán AP, Márquez FPG. Improving the efficiency on decision

making process via BDD. In:

[5] Pliego Marugán A, García Márquez FP, Lev B. Optimal

decision-making via binary decision diagrams for investments under a risky environment. International Journal of Production Research.

[6] Ray A, Tangirala S. Stochastic modeling of fatigue crack dynamics for on-line failure prognostics. IEEE Transactions on Control Systems Technology. 1996;**4**:443-451

[7] Márquez FPG, Zaman N. Digital Filters and Signal Processing. Intech;

[8] Márquez FPG. Digital Filters. Intech;

[9] Ho S, Xie M. The use of Arima models for reliability forecasting and analysis. Computers and Industrial Engineering. 1998;**35**:213-216

2017. pp. 1377-1388

2015. pp. 1395-1405

2017;**55**:5271-5286

Conference on Management Science and Engineering Management. Springer;

Proceedings of the Ninth International Conference on Management Science and Engineering Management. Springer;

[2] Pérez JMP, Asensio ES,

**References**

*Introductory Chapter: Prognostics - An Overview DOI: http://dx.doi.org/10.5772/intechopen.86894*
