Intelligent model-based diagnostics for vehicle health management

Jianhui Luo; Fang Tu; Mohammad Shafiul Azam; Krishna R. Pattipati; Peter K. Willett; Liu Qiao; Masayuki Kawamoto

doi:10.1117/12.498770

8 August 2003 Intelligent model-based diagnostics for vehicle health management

Jianhui Luo, Fang Tu, Mohammad Shafiul Azam, Krishna R. Pattipati, Peter K. Willett, Liu Qiao, Masayuki Kawamoto

Proceedings Volume 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III; (2003) https://doi.org/10.1117/12.498770
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

Citation Download Citation

Jianhui Luo, Fang Tu, Mohammad Shafiul Azam, Krishna R. Pattipati, Peter K. Willett, Liu Qiao, and Masayuki Kawamoto "Intelligent model-based diagnostics for vehicle health management", Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); https://doi.org/10.1117/12.498770

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available