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Nikunj Oza

Member since: Sep 30, 2010, NASA

Anomaly Detection in a Fleet of Systems

Shared by Nikunj Oza, updated on Jul 21, 2012

Summary

Author(s) :
Nikunj C. Oza, Santanu Das
Abstract

A fleet is a group of systems (e.g., cars, aircraft) that are designed and manufactured the same way and are intended to be used the same way. For example, a fleet of delivery trucks may consist of one hundred instances of a particular model of truck, each of which is intended for the same type of service—almost the same amount of time and distance driven every day, approximately the same total weight carried, etc. For this reason, one may imagine that data mining for fleet monitoring may merely involve collecting operating data from the multiple systems in the fleet and developing some sort of model, such as a model of normal operation that can be used for anomaly detection. However, one then may realize that each member of the fleet will be unique in some ways—there will be minor variations in manufacturing, quality of parts, and usage. For this reason, the typical machine learning and statis- tics algorithm’s assumption that all the data are independent and identically distributed is not correct. One may realize that data from each system in the fleet must be treated as unique so that one can notice significant changes in the operation of that system.

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Publication Name
Book Chapter, Machine Learning and Knowledge Discovery for Engineering Systems Health Management, CRC Press
Publication Location
N/A
Year Published
2011

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