Exploiting multiple mahalanobis distance metric to screen outliers from analogue product manufacturing test responses

One of the commonly used multivariate metrics for classifying defective devices from non-defective ones is Mahalanobis distance. This metric faces two major application problems: the absence of a robust mean and covariance matrix of the test measurements. Since the sensitivity of the mean and the covariance matrix is high in the presence of outlying test measurements, the Mahalanobis distance becomes an unreliable metric for classification. Multiple Mahalanobis distances are calculated from selected sets of test-response measurements to circumvent this problem. The resulting multiple Mahalanobis distances are then suitably formulated to derive a metric that has less overlap among defective and non-defective devices and which is robust to measurement shifts. This paper proposes such a formulation to both qualitatively screen product outliers and quantitatively measure the reliability of the non-defective ones. The resulting formulation is called Principal Component Analysis Mahalanobis Distance Multivariate Reliability Classifier (PCA-MD-MRC) Model. The application of the model is exemplified by an industrial automobile product.