Expert Guided Adaptive Maintenance
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Abstract
The heavy truck industry is a highly competitive business field; traditionally maintenance plans for heavy trucks are static and not subject to change. The advent of affordable telematics solutions has created a new venue for services that use information from the truck in operation. Such services could for example aim at improving the maintenance offer by taking into account information of how a truck has been utilized to dynamically adjust maintenance to align with the truck’s actual need. These types of services for maintenance are often referred to as condition based maintenance (CBM) and more recently Integrated Vehicle Health Management (IVHM).
In this paper we explain how we at Scania developed an expert system for adapting the maintenance intervals dependent on operational data from trucks. The expert system is aimed at handling components which maintenance experts have knowledge about but do not find it worth the effort to create a correct physical wear-model for.
We developed a systematic way for maintenance experts to express how operational data should influence the maintenance intervals. The rules in the expert system therefore are limited in what they can express, and as such our presented system differs from other expert systems in general.
In a comparison between our expert system and another general expert system framework, the expert system we constructed outperforms the general expert framework using our limited type of rules.
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Expert knowledge
Dunsdon, J., & Harrington, M. (2008). The Application of Open System Architecture for Condition Based Maintenance to Complete IVHM. Aerospace Conference, (pp. 1-9).
Durking, J. (1990). Application of Expert Systems in the Sciences. Ohio Journal of Science, 90(5), 171-179.
Ian K. Jennions et al. (2011). Integrated Vehicle Health Management: Perspectives on an Emerging Field. Warrendale: SAE.
Kowalski, R. A., & Sadri, F. (2009). Integrating Logic Programming and Production Systems in Abductive Logic Programming Agents. In Web Reasoning and Rule Systems (pp. 1-23). Berlin: Springer.
Marsh, C. A. (1988). The ISA expert system: a prototype system for failure diagnosis on the space station. Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems. 1, pp. 60-74. Tullahoma: ACM.
Mats Carlsson et al. (2013). SICStus Prolog Users's Manual. Kista: Intelligent Systems Laboratory, Swedish Institute of Computer Science.
Mitchell, T. (1997). Machine Learning. New York: McGraw-Hill.
Red Hat. (2013, 09 25). Drools. (Red Hat) Retrieved 09 25, 2013, from http://labs.jboss.com/drools
Russel, S. J., & Norvig, P. (2010). Artificial Intelligence - A Modern Approach (3rd edition). Upper Saddle River: Pearson Education.
Senlin Liang, et al. (2009). OpenRuleBench: an analysis of the performance of rule engines. Proceedings of the 18th international conference on World Wide Web. Madrid.
Shapiro, S. C. (1987). Processing, bottom-up and top-down. In Encyclopedia of Artificial Intelligence (pp. 779-785). New York: John Wiley & Sons.
W3C. (2013, 02 05). RIF RULE INTERCHANGE FORMAT CURRENT STATUS. (W3C) Retrieved 08 21, 2013, from http://www.w3.org/standards/techs/rif#w3c_all
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