A Robust Metric for Screening Outliers from Analogue Product Manufacturing Tests Responses

Shaji Krishnan, Shaji Krishnan, Hans G. Kerkhoff

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)

    Abstract

    Mahalanobis distance is one of the commonly used multivariate metrics for finely segregating defective devices from non-defective ones. An associated problem with this approach is the estimation of a robust mean and a covariance matrix. In the absence of such robust estimates, especially in the presence of outliers to test-response measurements, and only a sub-sample from the population is available, the distance metric becomes unreliable. To circumvent this problem, multiple Mahalanobis distances are calculated from selected sets of test-response measurements. They are then suitably formulated to derive a metric that has a reduced variance and robust to shifts or deviations in measurements. In this paper, such a formulation is proposed to qualitatively screen product outliers and quantitatively measure the reliability of the non-defective ones. The application of method is exemplified over a test set of an industrial automobile product.
    Original languageUndefined
    Title of host publicationProceedings 16th IEEE European Test Symposium, ETS 2011
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages159-164
    Number of pages6
    ISBN (Print)978-1-4577-0483-3
    DOIs
    Publication statusPublished - 23 May 2011
    Event16th IEEE European Test Symposium, ETS 2011 - Trondheim, Norway
    Duration: 23 May 201127 May 2011
    Conference number: 16

    Publication series

    Name
    PublisherIEEE Computer Society
    ISSN (Print)1530-1877

    Conference

    Conference16th IEEE European Test Symposium, ETS 2011
    Abbreviated titleETS
    CountryNorway
    CityTrondheim
    Period23/05/1127/05/11

    Keywords

    • METIS-284958
    • IR-79215
    • manufacturing test
    • EWI-21152
    • Metrics
    • CAES-TDT: Testable Design and Test
    • Outliers

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