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Proceedings Paper

MTS in false positive reduction for multi-sensor fusion
Author(s): Robert Woodley; Michael Gosnell; Elizabeth Cudney
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Paper Abstract

The Mahalanobis Taguchi System (MTS) is a relatively new tool in the vehicle health maintenance domain, but has some distinct advantages in current multi-sensor implementations. The use of Mahalanobis Spaces (MS) allows the algorithm to identify characteristics of sensor signals to identify behaviors in machines. MTS is extremely powerful with the caveat that the correct variables are selected to form the MS. In this research work, 56 sensors monitor various aspects of the vehicles. Typically, using the MTS process, identification of useful variables is preceded by validation of the measurements scale. However, the MTS approach doesn’t directly include any mitigating steps should the measurement scale not be validated. Existing work has performed outlier removal in construction of the MS, which can lead to better validation. In our approach, we modify the outlier removal process with more liberal definitions of outliers to better identify variables’ impact prior to identification of useful variables. This subtle change substantially lowered the false positive rate due to the fact that additional variables were retained. Traditional MTS approaches identify useful variables only to the extent they provide usefulness in identifying the positive (abnormal) condition. The impact of removing false negatives is not included. Initial results show our approach can reduce false positive values while still maintaining complete fault identification for this vehicle data set.

Paper Details

Date Published: 22 May 2014
PDF: 12 pages
Proc. SPIE 9121, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014, 912102 (22 May 2014); doi: 10.1117/12.2050527
Show Author Affiliations
Robert Woodley, 21st Century Systems, Inc. (United States)
Michael Gosnell, 21st Century Systems, Inc. (United States)
Elizabeth Cudney, 21st Century Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 9121:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014
Jerome J. Braun, Editor(s)

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