2014 PredictiveAnalysisofEngineHealt

(Mukherjee et al., 2014) ⇒ Shubhabrata Mukherjee, Aparna S. Varde, Giti Javidi, and Ehsan Sheyban. (2014). “Predictive Analysis of Engine Health for Decision Support.” In: ACM SIGKDD Explorations Newsletter Journal, 15(2). doi:10.1145/2641190.2641197

Subject Headings: Decision-Making Task, Preventive Maintenance.

Notes

Data mining, the discovery of knowledge from data, bridges several disciplines such as database management, artificial intelligence, statistics, visualization and the domain of the data, e.g., biology or engineering. Knowledge discovered by mining the data can be used for various purposes such as developing decision support systems and intelligent tutors. In this paper we present such a data mining problem in the mechanical engineering domain where knowledge discovery from the data is performed using statistical approaches, to conduct predictive analysis for decision support. More specifically, we focus on the engine health problem which consists of using existing data on the behavior of an engine in order to predict whether the engine is capable of functioning well (i.e., it is healthy) and to offer suggestions on preventive maintenance. The data we use for this predictive analysis consists of graphs that plot process parameters such as the vibration and temperature of the engine with respect to time. In this paper we define the problem in detail, propose a solution based on statistical inference techniques, summarize our experimental evaluation and discuss the applications of this work in various fields from a decision support angle.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 PredictiveAnalysisofEngineHealt	Shubhabrata Mukherjee Aparna S. Varde Giti Javidi Ehsan Sheyban			Predictive Analysis of Engine Health for Decision Support				10.1145/2641190.2641197		2014