Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering FLYWHEEL : Jurnal Teknik Mesin Untirta
Putra, Arifrahman Yustika
Unknown Affiliation
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Viscous Damping Coefficient Measurement System Using Incremental Optical Encoder Sudarmaji, Arief; Putra, Arifrahman Yustika; Yudiarsah, Efta
FLYWHEEL : Jurnal Teknik Mesin Untirta Volume 9, Issue 1, April 2023
Publisher : Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36055/fwl.v0i0.19525

Abstract

We built a viscous damping coefficient measurement system that applies the principles of underdamped harmonic oscillation within viscous liquid. The scientific novelty of this paper lies on the type of sensor used to capture the oscillation. An incremental optical encoder is chosen as a motion detector due to its ability to convert angular position as well as rotation direction into a pair of square wave signals. The harmonic oscillator system consists of a spring with the spring constant value of 82.8 N/m, a 1.50 kg cylindrical mass and a 95.0 g prolate ellipsoidal mass. The data acquisition system converts the encoder output pulses into counts which represent the displacements of the oscillating mass under viscous liquid. We used a Proportional Integral Derivative (PID) temperature control system to maintain the sample temperature at a constant value. Experimental data suggests that the air resistance and the total friction of the mechanical components give good contribution to the damping effect of the mass’ harmonic oscillation. The repeatability test of the viscous damping coefficient measurement resulted 1.26975744 % of relative standard deviation
Machine learning-based predictive maintenance framework for seismometers: is it possible? Putra, Arifrahman Yustika; Lestari, Titik; Saputro, Adhi Harmoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp187-205

Abstract

Seismometers are crucial in earthquake and tsunami early warning systems, since they record ground vibrations due to significant seismic events. The health condition of a seismometer is strongly related to the measurement of seismic data quality, making seismometer health condition maintenance critical. Predictive maintenance is the most advanced control or measurement system maintenance method, since it informs about the faults that have occurred in the system and the remaining lifetime of the system. However, no research has proposed a seismometer predictive maintenance framework. Thus, this article reviews general predictive maintenance methods and seismic data quality analysis methods to find the feasibility of developing a predictive maintenance framework for seismometers in seismic stations. Based on the review, it is found that such a framework can be built under particular challenges and requirements. Finally, machine learning is the best approach to build the classification and regression models in the predictive maintenance framework due to its robustness and high prediction accuracy.
Co-Authors Adhi Harmoko Saputro Efta Yudiarsah, Efta Sudarmaji, Arief Titik Lestari