2014 LogbasedPredictiveMaintenance

• (Sipos et al., 2014) ⇒ Ruben Sipos, Dmitriy Fradkin, Fabian Moerchen, and Zhuang Wang. (2014). “Log-based Predictive Maintenance.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623340

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• http://scholar.google.com/scholar?q=%222014%22+Log-based+Predictive+Maintenance
• http://dl.acm.org/citation.cfm?id=2623330.2623340&preflayout=flat#citedby

Quotes

Author Keywords

• Crisp-dm; industrial automation; log mining; machine learning; predictive maintenance

Abstract

Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 LogbasedPredictiveMaintenance	Fabian Moerchen Dmitriy Fradkin Zhuang Wang Ruben Sipos			Log-based Predictive Maintenance				10.1145/2623330.2623340		2014