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Main Title: Impact detection using a machine learning approach and experimental road roughness classification
Author(s): Gorges, Christian
Öztürk, Kemal
Liebich, Robert
Type: Article
Language Code: en
Is Part Of: 10.14279/depositonce-7679
Abstract: First, this publication presents the experimental validation of a road roughness classification method. Second, an impact detection strategy for two-wheeled vehicles is proposed including a classification of service loads, mild special events, and severe special events. The methods presented utilise the vehicle’s onboard signals to gather field data. The modular road roughness classification system operates with the vehicle’s transfer functions, and continuously classifies the road profile, according to ISO 8608. The method was successfully validated on test tracks with known road profiles. The impact detection strategy was developed using a supervised machine learning technique. Six road obstacles were ridden over using different velocities to invoke mild and severe special events. The most popular classifiers were trained for comparison and prediction of future observations. The developed impact detection strategy shows a high accuracy and was successfully validated using a k-fold cross-validation. The combination of the road roughness classification system and the impact detection strategy, enables a holistic field data acquisition of customer usage profiles, in the context of durability engineering. The collection of customer usage profiles improves vehicle design targets and enables a virtual load acquisition.
Issue Date: 2018
Date Available: 29-Jan-2019
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): Road roughness classification
ISO 8608
Impact detection
Supervised machine learning
Customer usage profiles
Two-wheeled vehicles
Journal Title: Mechanical Systems and Signal Processing
Publisher: Elsevier
Publisher Place: Amsterdam
Volume: 117
Publisher DOI: 10.1016/j.ymssp.2018.07.043
Page Start: 738
Page End: 756
EISSN: 1096-1216
ISSN: 0888-3270
Appears in Collections:Inst. Maschinenkonstruktion und Systemtechnik » Publications

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