Exploring the Mahalanobis-Taguchi approach to extract vehicle prognostics and diagnostics

Abstract

Army logistical systems and databases contain massive amounts of data that require effective methods of extracting actionable information and generating knowledge. Vehicle diagnostics and prognostics can be challenging to analyze from the Command and Control (C2) perspective, making management of the fleet difficult within existing systems. Databases do not contain root causes or the case-based analyses needed to diagnose or predict breakdowns. 21st Century Systems, Inc. previously introduced the Agent-Enabled Logistics Enterprise Intelligence System (AELEIS) to assist logistics analysts with assessing the availability and prognostics of assets in the logistics pipeline. One component being developed within AELEIS is incorporation of the Mahalanobis-Taguchi System (MTS) to assist with identification of impending fault conditions along with fault identification. This paper presents an analysis into the application of MTS within data representing a known vehicular fault, showing how construction of the Mahalanobis Space using competing methodologies can lead to reduced false positives while still capturing true positive fault conditions. These results are then discussed within the larger scope of AELEIS and the resulting C2 benefits.