Application of Machine Learning for Predictive Maintenance Cooling System in Nam Ngum-1 Hydropower Plant

Abstract

This paper presents machine learning (ML) application for predictive maintenance of a water cooling system in Nam Ngum-1 (NNG-1) hydropower plant located in Vientiane province, Lao PDR. Data used for the learning algorithm is from log sheets 31 months, compiled by a temperature in/out heat exchanger unit and maintenance history. The data is separated into two sets: training and testing sets. This paper uses the Classification Learner Application to train model. The application supports 22 classifier types, which can be organized in six major classification algorithms including Decision Trees, Discriminant Analysis, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors (KNN), and Ensemble Classification. It was shown that the SVM and Decision Trees are better at predicting results compared to the other algorithms. Using ML with the recorded maintenance data demonstrated that the predictive maintenance could be done and provides good and acceptance criteria. The model helps operators to be at ease, with the ability to visualize and monitor the system.