TY - GEN
T1 - Pattern matching of in-vehicle acceleration time series data
AU - Vemulapalli, Pramod K.
AU - Ledva, Gregory S.
AU - Brennan, Sean N.
AU - Reichard, Karl M.
PY - 2012
Y1 - 2012
N2 - This paper presents a novel approach to find patterns in vehicle x-y-z acceleration data for use in prognostics and diagnostics. In this problem, vehicles are assumed to travel on the same routes and often times as a part of convoys but their GPS and other position information has been removed for privacy reasons. The goal of the pattern matching scheme is to identify the route or convoy associations within vehicles by using the acceleration data collected onboard these vehicles. A crucial step in solving this problem is to choose the right feature vector, as raw matching of acceleration signals is inappropriate due to different velocities, driving behaviors, vehicle loading, etc. In this paper, we demonstrate the feasibility of using 'Multi-Scale Extrema Features' for this application. The paper also addresses implementation details to enhance performance for in-vehicle acceleration data, corrupted by different sources of noise. A novel 'Multi-Scale Encoding' method is also proposed for the above feature vector and it leads to a significant improvement in the performance over traditional pattern matching methods. While the main focus of the paper is towards identifying feature vectors that effectively describe in-vehicle acceleration data, the feature vector could potentially be used with acceleration data obtained from other applications.
AB - This paper presents a novel approach to find patterns in vehicle x-y-z acceleration data for use in prognostics and diagnostics. In this problem, vehicles are assumed to travel on the same routes and often times as a part of convoys but their GPS and other position information has been removed for privacy reasons. The goal of the pattern matching scheme is to identify the route or convoy associations within vehicles by using the acceleration data collected onboard these vehicles. A crucial step in solving this problem is to choose the right feature vector, as raw matching of acceleration signals is inappropriate due to different velocities, driving behaviors, vehicle loading, etc. In this paper, we demonstrate the feasibility of using 'Multi-Scale Extrema Features' for this application. The paper also addresses implementation details to enhance performance for in-vehicle acceleration data, corrupted by different sources of noise. A novel 'Multi-Scale Encoding' method is also proposed for the above feature vector and it leads to a significant improvement in the performance over traditional pattern matching methods. While the main focus of the paper is towards identifying feature vectors that effectively describe in-vehicle acceleration data, the feature vector could potentially be used with acceleration data obtained from other applications.
UR - http://www.scopus.com/inward/record.url?scp=84885943692&partnerID=8YFLogxK
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U2 - 10.1115/DSCC2012-MOVIC2012-8758
DO - 10.1115/DSCC2012-MOVIC2012-8758
M3 - Conference contribution
AN - SCOPUS:84885943692
SN - 9780791845301
T3 - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
SP - 761
EP - 770
BT - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
T2 - ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
Y2 - 17 October 2012 through 19 October 2012
ER -