Suspension Fault Diagnostics Using Vehicle Pitch and Roll Models
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The vehicle suspension system, including springs, dampers and stabilizer bars are critical to vehicle riding and handling experience. Automatic fault detection, isolation and failure prognosis of the suspension system will greatly improve vehicle perceived quality, serviceability and customer experience. In our previous work, a static diagnostic approach using a ramp with the known slope is proposed. Even though the method can effectively isolate the suspension system faults to each vehicle corner, it requires additional setups at dealerships. In this work, a passive approach using the vehicle pitch and roll models is presented, which can accurately isolate broken springs, leaking dampers, and broken stabilizer bars. Some enabling conditions are proposed to improve the overall algorithm robustness. The proposed solution is verified using the data collected from a test vehicle.
How to Cite
##plugins.themes.bootstrap3.article.details##
suspension, Diagnostics, vehicle
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.