Nikunj C. Oza's Publications

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Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management Using Space Shuttle Main Engine Data

Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management Using Space Shuttle Main Engine Data. Rodney A. Martin, Mark Schwabacher, Nikunj C. Oza, and Ashok N. Srivastava. In JANNAF Propulsion Meeting, 2007.

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Abstract

Several different unsupervised anomaly detection algorithms have been applied to Space Shuttle Main Engine (SSME) data to serve the purpose of developing a comprehensive suite of Integrated Systems Health Management (ISHM) tools. As the theoretical bases for these methods vary considerably, it is reasonable to conjecture that the resulting anomalies detected by them may differ quite significantly as well. As such, it would be useful to apply a common metric with which to compare the results. However, for such a quantitative analysis to be statistically significant, a sufficient number of examples of both nominally categorized and anomalous data must be available.Due to the lack of sufficient examples of anomalous data, use of any statistics that rely upon a statistically significant sample of anomalous data is infeasible. Therefore, the main focus of this paper will be to compare actual examples of anomalies detected by the algorithms via the sensors in which they appear, as well the times at which they appear. We find that there is enough overlap in detection of the anomalies among all of the different algorithms tested in order for them to corroborate the severity of these anomalies. In certain cases, the severity of these anomalies is supported by their categorization as failures by experts, with realistic physical explanations. For those anomalies that can not be corroborated by at least one other method, this overlap says less about the severity of the anomaly, and more about the technical nuances of the algorithms, which will also be discussed.

BibTeX Entry

@inproceedings{masc07,
	author = {Rodney A. Martin, Mark Schwabacher, Nikunj C. Oza, and Ashok N. Srivastava},
	title = {Comparison of Unsupervised Anomaly Detection Methods for Systems Health Management Using Space Shuttle Main Engine Data},
	booktitle = {JANNAF Propulsion Meeting},
	abstract = {Several different unsupervised anomaly detection algorithms have been applied to Space Shuttle Main Engine (SSME) data to serve the purpose of developing a comprehensive suite of Integrated Systems Health Management (ISHM) tools. As the theoretical bases for these methods vary considerably, it is reasonable to conjecture that the resulting anomalies detected by them may differ quite significantly as well. As such, it would be useful to apply a common metric with which to compare the results. However, for such a quantitative analysis to be statistically significant, a sufficient number of examples of both nominally categorized and anomalous data must be available.
Due to the lack of sufficient examples of anomalous data, use of any statistics that rely upon a statistically significant sample of anomalous data is infeasible. Therefore, the main focus of this paper will be to compare actual examples of anomalies detected by the algorithms via the sensors in which they appear, as well the times at which they appear. We find that there is enough overlap in detection of the anomalies among all of the different algorithms tested in order for them to corroborate the severity of these anomalies. In certain cases, the severity of these anomalies is supported by their categorization as failures by experts, with realistic physical explanations. For those anomalies that can not be corroborated by at least one other method, this overlap says less about the severity of the anomaly, and more about the technical nuances of the algorithms, which will also be discussed.}
	bibt2html_pubtype = {Other Conference},
	bib2html_rescat = {Fault Detection},
	year = {2007}
}

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