Extraction of Causalities and Rules Involved in Wear of Machinery from Lubricating Oil Analysis Data

Recently, methods in order to diagnose wear conditions of the equipments have been established. Lublicating oil analysis is one of these methods. However, since relations between events in wear are complex, its diagnosis relies on judgment by experts at this moment. In order to solve this problem, a purpose of this study is to support its diagnosis by generating a automatic diagnosis model. In this paper, we proposed a method that generate the model in order to predict wear conditions of the equipments. First, the causalities in wear were extracted from the diagnosis reports which experts described considerations for wear conditions of the equipments using text mining. Second, the equipments which has similar features were classi?ed using clustering and the rule of each cluster was extracted using decision tree from analysis data related to lubricating oil and equipments. Finally, the models were generated by combining the causalities and the rules. Although the results of evaluation indicated that automatic diagnosis is possible, it will be necessary to diagnose the more detailed wear conditions of machinery in the future tasks.