PENERAPAN MACHINE LEARNING DALAM PREDICTIVE MAINTENANCE PADA UNIT INTERCOOLER TURBO MESIN INDUK NKK SEMT-PIELSTICK 12PC4-2V-570 PADA KAPAL PASSENGER

Abstract

Predictive maintenance (PdM) is used to predict when equipment may fail and assist in
providing adequate monitoring and maintenance planning before potential future failures. In this
research there are several topics of discussion regarding predictive maintenance on the NKK-SEMT
PIELSTICK 12PC4-2V-570 main engine turbo intercooler unit on passenger ships including how to
model predictive maintenance using machine learning using supervised learning methods, namely
decision tree and random forest (rf). Decision tree, then what models and methods have a high accuracy
value and finally to get the operating hours of the equipment until it fails. The decision tree approach
is used to predict category or class labels and construct knowledge based on training data and labels.
Random forest is a collection of decision trees generated from random sample selection with different
node splitting rules. This model utilises a subset of the features in each tree, then finds the best threshold
to split the data. Through turbo system performance anomalies, the use of machine learning with
supervised learning methods can process previously classified data to get output in the form of
predictive maintenance. Based on the results of predictive maintenance modelling, the decision tree
method is the most accurate method with a ratio of training data and testing data division of 60:40
resulting in the highest accuracy. With MAE value 0, RMSE 0, RAE 0%, TP rate 1, FP rate 0, recall 1,
F-measure 1, and Accuracy 100%. By using a decision tree model with a data set comparison ratio of
60:40, predict fututre is performed, the equipment is predicted to fail after operating for the next 3112
hours.