Engine replacement scheduling optimization using Data Mining

Abstract

Engine overhaul activity in heavy duty equipment takes long shutdown duration, while unscheduled replacement is impacted on process delay, increasing man power cost, and production loss. One main cause of these problems is the scheduling performed just based on mechanics’ intuition and experience. On the other side, condition monitoring data are available in a large number. Reliable data processing methods are needed to disclose hidden information from the data. For the purpose, this research used three data mining methods on condition monitoring data and external factors of heavy equipment engine to get an optimized engine replacement scheduling. Clustering method was used to classify condition monitoring data, association rule was used to analyze the interrelationship between variables and time series analysis was used to predict the value of condition monitoring. The result showed that data mining methods can be used to perform scheduling optimization. Unscheduled replacement engine or engine failed in service was reduced from 31% to 13%.