Leak Detection, Localization, and Prognosis of High Pressure Fuel Delivery System
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Spark Ignition Direct Injection (SIDI) technology enables better fuel economy and tail pipe emissions in vehicles equipped with gasoline engines. The SIDI technology relies on the system’s ability to deliver fuel at high pressures (20-40 MPa). Such high pressure systems are prone to leakage if subjected to excessive vibrations, improper fitting, or failure of pressure seals over time due to cyclical loading. Fuel leakage can directly affect the operation of the engine and can cause customer inconvenience. It, therefore, becomes very important to devise a scheme that can effectively diagnose and prognose such kind of system fault. In this report, algorithm development for diagnosis and prognosis of leaks in high pressure fuel delivery system is presented. In particular, pressure profile of fuel in the common rail at engine cranking and engine shutdown are studied to generate schemes for fault detection, fault isolation, and fault prediction. The developed results are equally applicable to direct injection diesel engines given their similarity of operating principles and components.
How to Cite
##plugins.themes.bootstrap3.article.details##
Prognosis, Anomaly Detection, Fault Isolation, Fault Prediction, SIDI, SIDI Leak Detection, GDI, GDI Leak Detection
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.