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.
- Dynamic Systems and Control Division
Pattern Matching of In-Vehicle Acceleration Time Series Data
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Vemulapalli, PK, Ledva, GS, Brennan, SN, & Reichard, KM. "Pattern Matching of In-Vehicle Acceleration Time Series Data." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 761-770. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8758
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