Prediction of Lubricant Service Life Using Data Mining to Improve Reliability of Water Injection Pumps in Crude Oil Production Facility

Hermawan, Yusuf and Danang Wijaya, F. and Setiawan, Noor Akhmad and Dharmastiti, Rini (2021) Prediction of Lubricant Service Life Using Data Mining to Improve Reliability of Water Injection Pumps in Crude Oil Production Facility. In: 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 2 October 2021, Malang.

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Abstract

Predicting service life of lubricant based on lubricant condition data can help identifying accelerated wear indicating reliability problem in machinery. Data mining has been implemented for prediction in various fields including in petroleum industries to support decision making during the reliability improvements of crude oil production facilities. This research aimed to predict the lubricant service life of water injection pumps using several data mining algorithms, such as Linear Regression, Multilayer Perceptron (MLP), Gradient Boosting, and Random Forest. As results, the predicted service life values have a good correlation with the actual service life. The Gradient Boosting and Random Forest algorithms show better performance, with R2 of 0.71 and 0.74, comparing with the Linear Regression and MLP algorithms (R2=0.3 and 0.58), respectively. The RMSE values of Gradient Boosting and Random Forest algorithms (29.25 and 27.59) also show smaller errors than the two other algorithms (45.71 and 35.37). The results also confirmed that the Random Forest algorithm is slightly better than the Gradient Boosting algorithm. The decision tree of the prediction rule also can be shown by the Random Forest algorithm. © 2021 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 1
Uncontrolled Keywords: Crude oil; Data mining; Decision trees; Forecasting; Machinery; Multilayers; Reliability analysis; Service life; Condition; Crude oil production; Gradient boosting; Injection pump; Lubricant analysis; Multilayers perceptrons; Production facility; Random forest algorithm; Random forests; Water pump; Linear regression
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Electronics Engineering Department
Depositing User: Sri JUNANDI
Date Deposited: 10 Oct 2024 02:49
Last Modified: 10 Oct 2024 02:49
URI: https://ir.lib.ugm.ac.id/id/eprint/8659

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