@inproceedings{ee639515465343c89ac79a0d4665e16a,
title = "Fault detection system using machine learning on geothermal power plant",
abstract = "Geothermal power plants are a renewable clean energy source with great potential that Indonesia has. The manual fault detection system at the critical machine is one of the problems in the operation of geothermal power plants in Indonesia. Vulnerable errors in determining engine conditions and delays in knowing alerts are two major problems that arise. The application of machine learning algorithms in making fault detection models has been used in various industries and objects. This research is the application of machine learning algorithms to create fault detection classification models on critical engines of geothermal power plants. The algorithm used is the basic classifier and ensemble classifier to compare which algorithms produce the best classification indicators of classifications. This research can provide insight into the geothermal power plant industry in Indonesia to overcome existing fault detection system by utilizing sensor data using machine learning algorithm.",
keywords = "Classification Model, Fault Detection, Geothermal Power Plant, Machine Learning",
author = "Zulkarnain and Isti Surjandari and Bramasta, {Resha Rafizqi} and Enrico Laoh",
year = "2019",
month = jul,
doi = "10.1109/ICSSSM.2019.8887710",
language = "English",
series = "2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019",
address = "United States",
note = "16th International Conference on Service Systems and Service Management, ICSSSM 2019 ; Conference date: 13-07-2019 Through 15-07-2019",
}
