Abstract
Recommendation engine's techniques have proved their performance in different fields such as Amazon and Netflix. This paper discusses the usage of the recommendation engine concept in the industrial field, especially in maintenance operations. Nowadays, the plant maintenance team needs to make a maintenance plan against sudden asset failure, to reduce unscheduled production downtime. However, the planning takes a lot of time, because the appropriate maintenance countermeasures are chosen from many options depending on the failure condition and asset environment. Therefore, we try to suggest a reliable countermeasure against the failure conditions to make the planning time short. In this work, we propose two approaches for the maintenance recommender systems based on artificial intelligence techniques to recommend the maintenance actions. The first approach is a single-stage recommender system that reads the defect information and its description entered by the operator to recommend the maintenance action for similar defects found in the historical data. The second approach is a multi-stage recommender system where the system starts by estimating one of the maintenance attributes which be used as an input for the next stage to estimate the next maintenance attribute. Finally, we will evaluate the accuracy of the recommendation by using past maintenance report which contains defect condition and maintenance actions adopted actually in the past. We found that the multi-stage system outperformed the single-stage system in terms of accuracy, and the multistage system is possibly helped the maintenance team against the sudden asset failure with the maintenance action recommendation.