MTS in False Positive Reduction for Multi-Sensor Fusion
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.
R. S. Woodley et al., "MTS in False Positive Reduction for Multi-Sensor Fusion," Proceedings of SPIE - The International Society for Optical Engineering, vol. 9121, SPIE, May 2014.
The definitive version is available at https://doi.org/10.1117/12.2050527
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014 (2014: May 6-7, Baltimore, MD)
Electrical and Computer Engineering
Engineering Management and Systems Engineering
Keywords and Phrases
Comparison of Methods; False Positive Reduction; Fault Detection; Mahalanobis-Taguchi System; Multi-Sensor Fusion; Threshold Selection; Variable Selection; Vehicle Health Maintenance
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