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A case study of a maintenance support system

One of the problems when maintaining a system installed at many sites and with many support centres, is that a lot of problems are reported several times, thus creating a large amount of extra work for the system maintenance organization. In order to reduce this problem, we have built a trouble report filter that will filter out more than 30% of all repeated trouble reports, even if the problem is described by different terms or at different system levels. The work consists of building a classification model based on the system description, terms used in the trouble reports, relations between these terms and by using measures for term distance and importance to compute a trouble report distance. The model can be tuned to maximum efficiency by varying the distance and importance measures. After describing the model and the term network, we describe two experiments with real data. The results show that it is possible to build a simple but highly efficient model for recognizing trouble reports.