Sachin Kumar, Eli Dolev, and Michael Pecht
Prognostics Health Management Group
Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland, College Park, MD 20742
This paper presents an innovative diagnostic approach that includes detection and fault isolation using the Mahalanobis distance (MD). The fault isolation
approach is based on the analysis of residual MD values corresponding to performance parameters. The residual value is calculated by taking the differences between MD
values estimated in two different scenarios: first, when a performance parameter is present, and second, when that
performance parameter is absent. The residual of MD values for each parameter is obtained by using training data
under several experiments planned by design-of-experiment
concept, to analyze impact of each parameter. The distribution of residual MD values for each parameter is analyzed and a 95% probabilistic range is established. This range represents the expected contribution by parameters towards healthy systems MDs, and it is used to identify parameters that are responsible for the anomalous behaviour of a system. Parameters that fall beyond the threshold limit are considered responsible for the anomalous behaviour, and the parameter that has lowest residual value is isolated as
the faulty parameter. A case study on notebook computers is presented to demonstrate and test the suggested new approach’s ability to isolate faulty parameters.
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