Abstract
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.
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