Artificial Intelligence
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A prototype for model-based on board diagnosis of automotive systems
AI Communications
Expert Systems Research Trends
Expert Systems Research Trends
Model-Based Failure Analysis with RODON
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Advanced Engineering Informatics
Advanced Engineering Informatics
Improved algorithms for deriving all minimal conflict sets in model-based diagnosis
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
A new strategy for automotive off-board diagnosis based on a meta-heuristic engine
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
The automotive after-sales dealers lack solutions for accurate, comprehensive and efficient fault localization. However, such services in the after-sales networks are crucial to the brand value of automotive manufacturers and for client satisfaction. In this paper, a new approach for the off-board diagnosis is presented, with significant improvements compared to the current technologies. A more robust approach that allows, per the additions of functional modules, to enhance traditional computer aided diagnostic systems towards knowledge based systems that emphasize the whole life cycle of the vehicle. Once the design of a new vehicle has begun, information like the dependencies between the components could be re-used for the models dedicated to the diagnosis task. The massive use of electronics dramatically increases the amount of data to manage for the testing of ECU (Electronic Control Unit) functionalities. The complexity of the sub-systems leads to breakdowns that need qualitative symptom description for fault localization. Finally, a feedback engine completes the expensive models for the diagnosis and returns critical dysfunctions to the design department. In this paper, we present our research on a global modular framework for the diagnosis. It encompasses the needs and requirements of automotive manufacturers. The results are presented with data obtained from low, middle and luxury class vehicles. They demonstrate the performance in real field conditions of our different modules. They are based on the interpretation of observations, the fault localization and isolation and the evaluation of feedbacks for model auto-completion. These experiments show the potential of our proposed approach for the automotive off-board diagnosis task.