Prediction of Maintenance Activities Using Generalized Sequential Pattern and Association Rules in Data Mining

Abstract

This study proposed a data mining framework for predicting sequential patterns of maintenance activities. The framework consisted of data collection, prediction of maintenance activities with and without attributes, and then the comparison between prediction results. In data collection, historical data were collected regarding maintenance activities and product attributes. The generalized sequential pattern (GSP) and association rules were then applied to predict maintenance activities with and without attributes to determine the frequent sequential patterns and significant rules of maintenance activities. Finally, a comparison was performed between the sequences of maintenance activities with and without attributes. A real case study of washing machine products was presented to illustrate the developed framework. The results showed that the proposed framework effectively predicted the next maintenance activities and planning preventive maintenance based on product attributes. In conclusion, the data mining approach is found effective in determining the maintenance sequence that reduces downtime and thereby enhancing productivity and availability.