@inproceedings{cline_predictive_2017,
	title = {Predictive maintenance applications for machine learning},
	doi = {10.1109/RAM.2017.7889679},
	abstract = {Machine Learning provides a complementary approach to maintenance planning by analyzing significant data sets of individual machine performance and environment variables, identifying failure signatures and profiles, and providing an actionable prediction of failure for individual parts.},
	booktitle = {2017 {Annual} {Reliability} and {Maintainability} {Symposium} ({RAMS})},
	author = {Cline, B. and Niculescu, R. S. and Huffman, D. and Deckel, B.},
	month = jan,
	year = {2017},
	keywords = {Analytical models, Connectors, Data models, Inspection, Machine Learning, Predicted Failure Analysis, Predictive Maintenance, Predictive maintenance, Predictive models, environmental variable, failure analysis, failure signature, learning (artificial intelligence), machine learning, machine performance, maintenance engineering, maintenance planning, planning, predictive maintenance, production engineering computing, reliability},
	pages = {1--7},
}

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