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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rekayasa Mesin Sinergi World Chemical Engineering Journal Jurnal Teknik Mesin Scientific Journal of Mechanical Engineering Kinematika Teknomekanik International Journal of Community Service Jurnal Abdimas ADPI Sains dan Teknologi International Journal of Innovation in Mechanical Engineering and Advanced Materials Ramatekno Journal of Engineering and Technological Sciences
Dedik Romahadi
Universitas Mercu Buana
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Journal : Sinergi

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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. 
Compound development as a protective layer on fecral substrate by a combination of γ-Al2O3 ultrasonic and NiO electroplating techniques to improve thermal stability Hidayat, Imam; Feriyanto, Dafit; Zakaria, Supaat; Abdulmalik, SS.; Nurato, Nurato; Romahadi, Dedik
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.003

Abstract

One of the most technologically advanced methods for developing and adhering catalysts to the FeCrAl substrate is electrophoretic deposition. However, it faces a problem: low thermal stability at high temperatures of 10000 °C, caused by a lack of a protective oxide layer. The goal of this study is to investigate the protective oxide layers formed by Al2O3 and NiO coatings on FeCrAl metallic material for catalytic converters (CATCO). The electrolyte was prepared with distilled water at a constant temperature of 40±50 °C. The pH was adjusted to 5 with HCl and NaOH reagents. The electrolyte was prepared at 40 ± 50 °C and stirred for 1 minute using a magnetic stirrer. A 50mm x 10mm Ni plate substrate served as the anode, while a 40mm x 20mm FeCrAl cathode was used. The spacing between the anode and cathode was set at 25mm. The electroplating was conducted for several variation times of 15, 30, 45, 60 and 75 minutes, current density of 8 A/dm2, 3g γ-Al2O3 was inserted into the beaker for each sample and the total surface area was 1600mm2 on both sides. Drying was performed after electroplating at 600 °C for 12 hours.  Raman spectroscopy revealed that several compounds observed during the experimental stages, such as FeCrAl, γ-Al2O3, NiO, NaO2, NiAl2O4, NiCr2O4, and FeCr2O3, were also present in the coated FeCrAl CATCO, with distinct peaks. Therefore, it can be concluded that the UB+EL 30 min successfully deposited the γ-Al2O3 and NiO on the FeCrAl substrate after CATCO fabrication.
Towards enhanced acoustic fan booster damage detection: a comparative study of feature-based and machine learning approaches Youlia, Rikko Putra; Romahadi, Dedik; Feleke, Aberham Genetu; Nugroho, Irfan Evi; Alina, Alina
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.016

Abstract

Machine failure detection frequently uses non-destructive monitoring techniques such as vibration analysis. Although vibration analysis can identify machine degradation, the apparatus is often costly and necessitates specialist knowledge. Additionally, many existing methods in audio classification rely on characteristics represented as pictures or vectors, which increases computational complexity. In contrast, this research introduces a novel method that substitutes vibration data with a singular numerical feature derived from audio signals, addressing both cost and complexity issues. Our objective is to develop a rapid and precise audio-based method for detecting machine damage. The acoustic signals from the machine apparatus were classified into three categories: normal, belt damage, and combined belt and bearing defect. The data processing technique involved lowering the sample rate and segmenting the data to improve computational efficiency and classification performance. We use the Welch method and appropriate statistical techniques to analyze Power Spectral Density (PSD). The performance of seven classifier models, KNN, LDA, SVM, NB, ANN, RF, and DT, was evaluated using accuracy, precision, sensitivity, specificity, and F-score. LDA achieved the highest accuracy at 92.83%, followed by ANN (92.75%), NB (92.74%), and DT (92.34%). These models outperformed KNN (89.90%) and RF (89.40%), with SVM recording the lowest accuracy at 85.40%. LDA was highly effective, achieving the highest accuracy with a single average PSD-type feature, showcasing its robustness in machine defect diagnosis. Compared to previous methods, this approach simplifies feature extraction, reduces computational demands, and maintains high diagnostic performance, providing notable benefits in terms of effectiveness and precision. 
Co-Authors A. M. Leman Abda Abda Abdul Hamid Abdulmalik, SS. Adinarto, Tri Wahyu Aditya, Bima Agung Wahyudi Biantoro Alief Avicenna Luthfie Alina, Alina Andi Firdaus Sudarma Anggara, Fajar Arvisyah, Diar Auf, Abdurrahman Chaeroni, Amat Dafit Feriyanto Desti Dorion, L. B. Diah Utami Diah Utami, Diah Enggar Pur nama , Rama Fajar Anggara Feleke, Aberham Genetu Genetu Feleke, Aberham Ghufron, Hanif Gian Villany Golwa Hadi Pranoto Hadi Pranoto Hadi Pranoto Hanif Ghufron Hifdzul Luthfan Habibullah Himawan S. Wibisono Hui Xiong Hui Xiong Hui Xiong I Gusti Ayu Arwati Ilhamullah, Ilhamullah Imam Hidayat Jalaluddin, Mai Nursherida Karmiadji, Djoko Wahyu Kurniawan, Rizki Nur Afami L. B. Desti Dorion Mahendra, Tito Syahril Sobarudin Izha Mahesh Kumar Maris, Iman Maulana Yusuf Md Radwanul Karim Muhamad Fitri Muhammad Imran Muhammad Imran Murtyas, Solli Dwi Nanang Ruhyat Noviana, Agus Nugroho, Irfan Evi Nurato Pramana, Putratama Aziz Rachmanu, Fatkur Rahmatullah, Rizky Rikko Putra Youlia Rizki Nur Afami Kurniawan Saputra, Gofar Julio Sisliana, Azara Vigha Solli Dwi Murtyas Sri Anah Subekti Supaat Zakaria Susilo, R. Dwi Pudji Turmudi, Agung Wang Dong Wermasaubun, Hendrikus Wibowo, Agus Setiawan Wijaya, Fathoni Putra Wiwit Suprihatiningsih Xiong, Hui Yafiq, Muhammad Sulthan Yang Xiawei Yishuang, Tang Yosua Heru Irawan Yudha Aji Pramono Zakaria Zakaria Zakaria, Supaat