@inproceedings{canizo_real-time_2017,
	title = {Real-time predictive maintenance for wind turbines using {Big} {Data} frameworks},
	doi = {10.1109/ICPHM.2017.7998308},
	booktitle = {2017 {IEEE} {International} {Conference} on {Prognostics} and {Health} {Management} ({ICPHM})},
	author = {Canizo, M. and Onieva, E. and Conde, A. and Charramendieta, S. and Trujillo, S.},
	year = {2017},
	keywords = {Apache Kafka, Apache Mesos, Apache Spark, Big Data, Big Data architectures, Big Data environment, Cloud computing, Companies, HDFS, Industry 4.0, Machine learning, O and M cost reduction, Predictive maintenance, Predictive models, Wind power, Wind turbines, centralized access point, cloud computing, data process speed, data-driven solution, failure analysis, fault-tolerant functionality, learning (artificial intelligence), maintenance engineering, monitoring agent, power engineering computing, predictive model generator, random forest algorithm, real-time predictive maintenance, wind turbines},
	pages = {70--77},
}

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