A residual estimation based approach for isolating faulty parameters

Abstract

This paper presents a new residual estimation based diagnostic approach that includes detection and fault isolation using the Mahalanobis distance (MD). The faulty performance parameter isolation approach is based on the analysis of residual MD values. The residual value is calculated by taking the difference 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 the MD values for each parameter is obtained by using training data from several experiments as part of the training data analysis planned by the design-of-experiment concept to analyze the 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 toward a healthy system's MDs, and it is used to identify the parameters that are responsible for the anomalous behavior of a system. Parameters that fall below the lower bound of the 95% probabilistic range are considered candidates for the anomalous behavior, and the parameter that has the lowest residual value is isolated as the faulty parameter. A case study on computers is presented to demonstrate and test the suggested new approach's ability to isolate faulty parameters.