Evaluation of Shock Detection Algorithm for Road Vehicle Vibration Analysis

Vibration

Rouillard, Vincent²  Lepine, Julien³ 

[1] Science, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia;College of Engineering &Department of Engineering, University of Cambridge, Trumptington St, Cambridge CB2 1PZ, UK

关键词: shock-on-r;    om;    environmental shock;    vehicle vibration;    shocks detection;    Receiver Operating Characteristic curve;    optimal operation point;    classifier calibration;   

DOI  :  10.3390/vibration1020016

学科分类：力学，机械学

来源: mdpi

【 摘 要 】

The ability to characterize shocks which occur during road transport is a vital prerequisite for the design of optimized protective packaging, which can assist in reducing cost and waste related to products and good transport. Many methods have been developed to detect shocks buried in road vehicle vibration signals, but none has yet considered the nonstationary nature of vehicle vibration and how, individually, they fail to accurately detect shocks. Using machine learning, several shock detection methods can be combined, and the reliability and accuracy of shock detection can also be improved. This paper presents how these methods can be integrated into four different machine learning algorithms (Decision Tree, k-Nearest Neighbors, Bagged Ensemble, and Support Vector Machine). The Pseudo-Energy Ratio/Fall-Out (PERFO) curve, a novel classification assessment tool, is also introduced to calibrate the algorithms and compare their detection performance. In the context of shock detection, the PERFO curve has an advantage over classical assessment tools, such as the Receiver Operating Characteristic (ROC) curve, as it gives more importance to high-amplitude shocks.

【 授权许可】

CC BY