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Proceedings Paper

Intelligent model-based diagnostics for vehicle health management
Author(s): Jianhui Luo; Fang Tu; Mohammad Shafiul Azam; Krishna R. Pattipati; Peter K. Willett; Liu Qiao; Masayuki Kawamoto
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Paper 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.

Paper Details

Date Published: 8 August 2003
PDF: 14 pages
Proc. SPIE 5107, System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, (8 August 2003); doi: 10.1117/12.498770
Show Author Affiliations
Jianhui Luo, Univ. of Connecticut (United States)
Fang Tu, Univ. of Connecticut (United States)
Mohammad Shafiul Azam, Univ. of Connecticut (United States)
Krishna R. Pattipati, Univ. of Connecticut (United States)
Peter K. Willett, Univ. of Connecticut (United States)
Liu Qiao, Toyota Technical Ctr. USA (United States)
Masayuki Kawamoto, Toyota Technical Ctr. USA (United States)

Published in SPIE Proceedings Vol. 5107:
System Diagnosis and Prognosis: Security and Condition Monitoring Issues III
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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