Exploring the Mahalanobis-Taguchi Approach to Extract Vehicle Prognostics and Diagnostics


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.

Meeting Name

2014 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems, CIVTS 2014 (2014: Dec. 9-12, Orlando, FL)


Computer Science

Second Department

Electrical and Computer Engineering

Keywords and Phrases

Analysis; Data; Experimentation; Information; Knowledge; Metrics; Modeling and Simulations

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2015