Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Sinergi Journal of Engineering and Technological Sciences
Feleke, Aberham Genetu
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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

Found 2 Documents
Search

 1 
Evaluation of FIR bandpass filter and Welch method implementation for centrifugal pump fault detection Romahadi, Dedik; Feleke, Aberham Genetu; Adinarto, Tri Wahyu; Feriyanto, Dafit; Biantoro, Agung Wahyudi; Rachmanu, Fatkur
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.007

Abstract

The motivation for this research is the high vibration observed during the operation of the centrifugal cooling water pump. Our study aims to assess the pump's state and check the vibrations to ensure the factors underlying the fault of the centrifugal pump in the alkaline chlorine factory. While previous studies have primarily used spectral amplitude results from the Fast Fourier Transform to analyze engine vibrations, we propose a different approach in this study. We employ the Finite Impulse Response (FIR) Bandpass Filter and the Welch Method, a practical analytic approach. The ISO 10816-3 standard is a benchmark of the RMS value to determine the pump's condition. The FIR Bandpass Filter and Welch Method prove to be highly effective in describing and modifying the vibrational signals of the centrifugal pump. The approach is particularly beneficial as it is consistent across sample rate settings, reduces the vibration of amplitude low, produces a smoother spectrum with only the primary frequency component, and segments the vibration signal into the frequency band-aids to identify the primary vibration source. The diagnostic results reveal increased vibrations at 1x, 2x, and ball pass frequency (BPF), indicating impeller damage and disappearance. Post-repair, the vibration value experiences a significant drop, as per the fault analysis results, further confirming the high effectiveness of our approach. These findings have practical implications for the maintenance and fault diagnosis of centrifugal pumps, providing a reliable and effective method for identifying and addressing issues. 
Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Feleke, Aberham Genetu
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.2

Abstract

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.
Co-Authors Adinarto, Tri Wahyu Agung Wahyudi Biantoro Dafit Feriyanto Rachmanu, Fatkur Romahadi, Dedik Wiwit Suprihatiningsih