Predictive maintenance magnetic sensor using random forest method

A. S. Aji, J. A. Sashiomarda, D. Handoko

Research output: Contribution to journalConference articlepeer-review

Abstract

BMKG has observations of earth's magnets which are scattered in Indonesia. The BMKG earth magnetic sensor produces real-time data output. This research focuses on predictive maintenance models on earth magnetic sensors based on data output. The resulting data output is in the form of a delimited format in the form of spaces so that it is easy to process. The magnetic component used is the total component data (F) from the earth's magnetic sensor. Data processing using python scripts with the algorithm used is the random forest method by comparing the resulting value difference to find out whether the data generated is still in tolerance or not. The results of the prediction model with the number of estimators 10 produce RF Score = 0.98 and MAE = 0.83.

Original languageEnglish
Article number012030
JournalJournal of Physics: Conference Series
Volume1528
Issue number1
DOIs
Publication statusPublished - 9 Jun 2020
Event4th International Seminar on Sensors, Instrumentation, Measurement and Metrology, ISSIMM 2019 - Padang, West Sumatera, Indonesia
Duration: 14 Nov 201914 Nov 2019

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