TY - GEN
T1 - Automotive damper defect detection using novelty detection methods
AU - Zehelein, Thomas
AU - Schuck, Sebastian
AU - Lienkamp, Markus
N1 - Publisher Copyright:
Copyright © 2019 ASME
PY - 2019
Y1 - 2019
N2 - With autonomous driving, the driver’s perceptual ability for irregularities in the chassis system will be decreased. Therefore, monitoring the chassis system for possible defects will be necessary. This paper analyzes the suitability of the four unsupervised learning algorithms for novelty detection Local Outlier Factor, Angle-based Outlier Detection, k-nearest neighbors and One-Class Support Vector Machine. The investigation is conducted using actual driving data with damper defects emulated using semi-active dampers. Aside from using manually generated features or using FFT datapoints as features, two automatically generated feature datasets using Autoencoder and Sparse Filter are investigated. Furthermore, the influence of different scaling methods and algorithm specific parameters is analyzed. Results show that a precision of up to 80 % is possible.
AB - With autonomous driving, the driver’s perceptual ability for irregularities in the chassis system will be decreased. Therefore, monitoring the chassis system for possible defects will be necessary. This paper analyzes the suitability of the four unsupervised learning algorithms for novelty detection Local Outlier Factor, Angle-based Outlier Detection, k-nearest neighbors and One-Class Support Vector Machine. The investigation is conducted using actual driving data with damper defects emulated using semi-active dampers. Aside from using manually generated features or using FFT datapoints as features, two automatically generated feature datasets using Autoencoder and Sparse Filter are investigated. Furthermore, the influence of different scaling methods and algorithm specific parameters is analyzed. Results show that a precision of up to 80 % is possible.
UR - http://www.scopus.com/inward/record.url?scp=85076437006&partnerID=8YFLogxK
U2 - 10.1115/DSCC2019-9188
DO - 10.1115/DSCC2019-9188
M3 - Conference contribution
AN - SCOPUS:85076437006
T3 - ASME 2019 Dynamic Systems and Control Conference, DSCC 2019
BT - Advanced Driver Assistance and Autonomous Technologies; Advances in Control Design Methods; Advances in Robotics; Automotive Systems; Design, Modeling, Analysis, and Control of Assistive and Rehabilitation Devices; Diagnostics and Detection; Dynamics and Control of Human-Robot Systems; Energy Optimization for Intelligent Vehicle Systems; Estimation and Identification; Manufacturing
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2019 Dynamic Systems and Control Conference, DSCC 2019
Y2 - 8 October 2019 through 11 October 2019
ER -