Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics

Gang Niu; Satnam Singh; Steven W. Holland; Michael Pecht

doi:10.1109/PHM.2010.5413608

Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics

Gang Niu^*, Satnam Singh, Steven W. Holland, Michael Pecht

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper presents a novel approach for anomaly detection of electronic products using the Mahalanobis Distance (MD) and Weibull distribution. The MD value is used as a health index, which has the advantage of both summarizing the multivariate operating parameters and reducing the data set into a univariate distance index. The Weibull distribution is used to determine health decision metrics, which are useful in characterizing distributions of MD values. Furthermore, a case study of the proposed notebook computer anomaly detection method is carried out The experimental results show that the proposed method is valuable. © 2010 IEEE

Original language	English
Title of host publication	2010 Prognostics and System Health Management Conference
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-4244-4758-9
ISBN (Print)	978-1-4244-4756-5
DOIs	https://doi.org/10.1109/PHM.2010.5413608
Publication status	Published - 2010
Event	2010 Prognostics and System Health Management Conference - Macao, China Duration: 12 Jan 2010 → 14 Jan 2010

Publication series

Name	Prognostics and System Health Management Conference
ISSN (Print)	2166-563X
ISSN (Electronic)	2166-5656

Conference

Conference	2010 Prognostics and System Health Management Conference
Place	China
City	Macao
Period	12/01/10 → 14/01/10

Funding

The work was supported by the Center for Prognostics and System Health Management at the City University of Hong Kong, and the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland, College Park.

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10.1109/PHM.2010.5413608

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@inproceedings{9f52b2eb970141d28c2fb4996d2caec0,

title = "Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics",

abstract = "This paper presents a novel approach for anomaly detection of electronic products using the Mahalanobis Distance (MD) and Weibull distribution. The MD value is used as a health index, which has the advantage of both summarizing the multivariate operating parameters and reducing the data set into a univariate distance index. The Weibull distribution is used to determine health decision metrics, which are useful in characterizing distributions of MD values. Furthermore, a case study of the proposed notebook computer anomaly detection method is carried out The experimental results show that the proposed method is valuable. {\textcopyright} 2010 IEEE",

author = "Gang Niu and Satnam Singh and Holland, {Steven W.} and Michael Pecht",

year = "2010",

doi = "10.1109/PHM.2010.5413608",

language = "English",

isbn = "978-1-4244-4756-5",

series = "Prognostics and System Health Management Conference",

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booktitle = "2010 Prognostics and System Health Management Conference",

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note = "2010 Prognostics and System Health Management Conference ; Conference date: 12-01-2010 Through 14-01-2010",

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Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics. / Niu, Gang; Singh, Satnam; Holland, Steven W. et al.
2010 Prognostics and System Health Management Conference. IEEE, 2010. (Prognostics and System Health Management Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics

AU - Niu, Gang

AU - Singh, Satnam

AU - Holland, Steven W.

AU - Pecht, Michael

PY - 2010

Y1 - 2010

N2 - This paper presents a novel approach for anomaly detection of electronic products using the Mahalanobis Distance (MD) and Weibull distribution. The MD value is used as a health index, which has the advantage of both summarizing the multivariate operating parameters and reducing the data set into a univariate distance index. The Weibull distribution is used to determine health decision metrics, which are useful in characterizing distributions of MD values. Furthermore, a case study of the proposed notebook computer anomaly detection method is carried out The experimental results show that the proposed method is valuable. © 2010 IEEE

AB - This paper presents a novel approach for anomaly detection of electronic products using the Mahalanobis Distance (MD) and Weibull distribution. The MD value is used as a health index, which has the advantage of both summarizing the multivariate operating parameters and reducing the data set into a univariate distance index. The Weibull distribution is used to determine health decision metrics, which are useful in characterizing distributions of MD values. Furthermore, a case study of the proposed notebook computer anomaly detection method is carried out The experimental results show that the proposed method is valuable. © 2010 IEEE

U2 - 10.1109/PHM.2010.5413608

DO - 10.1109/PHM.2010.5413608

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4244-4756-5

T3 - Prognostics and System Health Management Conference

BT - 2010 Prognostics and System Health Management Conference

PB - IEEE

T2 - 2010 Prognostics and System Health Management Conference

Y2 - 12 January 2010 through 14 January 2010

ER -

Anomaly Detection of Notebook Computer Based on Weibull Decision Metrics

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