Sachin Kumar, Member, IEEE
Prognostics and Health Management Laboratory,
Center for Advanced Life Cycle Engineering (CALCE),
University of Maryland,
College Park, MD 20742
Tommy W. S. Chow, Senior Member, IEEE
Prognostics and Health Management Centre,
Department of Electronic Engineering,
City University of Hong Kong,
Kowloon, Hong Kong.
Michael Pecht, Fellow, IEEE
Prognostics and Health Management Laboratory
CALCE, University of Maryland
College Park, MD 20742 USA
Prognostics and Health Management Center
City University of Hong Kong
Kowloon, Hong Kong
This paper presents a Mahalanobis distance (MD) based diagnostic approach that employs a probabilistic approach to establish thresholds to classify a product as being healthy or unhealthy. A technique for detecting trends and biasness in system health is presented by constructing a control chart for the MD value. The performance parameters’ residuals, which are the differences between the estimated values (from an empirical model) and the observed values (from health monitoring), are used to isolate parameters that exhibit faults. To aid in the qualification of a product against a specific known fault, we suggest that a fault-specific threshold MD value be defined by minimizing an error function. A case study on notebook computers is presented to demonstrate the applicability of this proposed diagnostic approach.
Keywords: Computers, diagnostics, electronic products, fault identification, fault isolation, Mahalanobis distance (MD).
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