Online fault detection and isolation of nonlinear systems based on neurofuzzy networks
Engineering Applications of Artificial Intelligence
Adding diagnostics to intelligent robot systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Case base management for analog circuits diagnosis improvement
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Real-valued negative selection algorithm with a Quasi-Monte Carlo genetic detector generation
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Fault diagnosis of analog circuits based on machine learning
Proceedings of the Conference on Design, Automation and Test in Europe
Multiple fault diagnosis approach for rotary machines based on matter-element
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Fault detection in analog circuits using a fuzzy dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Fault diagnostics in electric drives using machine learning
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. The purpose of fault diagnosis is the isolation of faults on defective systems, a task requiring a high skill set. This has driven the need for automated diagnostic tools. Over the last two decades, automated diagnosis has been an active research area, but the industrial acceptance of these techniques, particularly in cost-sensitive areas, has not been high. This paper reviews this research, primarily covering rule-based, model-based, and case-based approaches and applications. Future research directions are finally examined, with a concentration on issues, which may lead to a greater acceptance of automated diagnosis