Gang Niua, Satnam Singhb, Steven W. Hollandc, Michael Pechta d
a. Center for Prognostics and System Health Management, City University of Hong Kong,
83 Tat Chee Avenue, Kowloon, Hong Kong
b. Diagnosis and Prognosis Group, GM India Science Lab,
GM Global R&D, Bangalore, India
c. Electrical & Controls Integration Lab,
GM Global R&D, Warren, Michigan 49090, USA
d. Center for Advanced Life Cycle Engineering (CALCE), University of Maryland,
College Park, Maryland 20742, USA
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.
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