On-Board Particulate Filter Failure Prevention and Failure Diagnostics Using Radio Frequency Sensing

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications. The testing included controlled engine dynamometer evaluations, which characterized soot slip from various filter failure modes, as well as on-road fleet vehicle tests. The results show a high sensitivity to detect conditions resulting in soot leakage from the particulate filter, as well as potential for direct detection of structural failures including internal cracks and melted regions within the filter media itself. Furthermore, the measurements demonstrate, for the first time, the capability to employ a direct and continuous monitor of particulate filter diagnostics to both prevent and detect potential failure conditions in the field.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0950
Pages
16
Citation
Sappok, A., Ragaller, P., Herman, A., Bromberg, L. et al., "On-Board Particulate Filter Failure Prevention and Failure Diagnostics Using Radio Frequency Sensing," SAE Int. J. Engines 10(4):1667-1682, 2017, https://doi.org/10.4271/2017-01-0950.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0950
Content Type
Journal Article
Language
English