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All Journal WARTA JMPM (Jurnal Material dan Proses Manufaktur) Turbo : Jurnal Program Studi Teknik Mesin Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Journal of Robotics and Control (JRC) Journal of Community Service and Empowerment
Berli Paripurna Kamiel
Universitas Muhammadiyah Yogyakarta
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Journal : JMPM (Jurnal Material dan Proses Manufaktur)

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Pengembangan Metode Deteksi Cacat Bantalan Berbasis Analisis Envelope pada Prototipe Fan Industri Berli Paripurna Kamiel; Adib Muhammad Nuh; Sudarisman Sudarisman
JMPM (Jurnal Material dan Proses Manufaktur) Vol 2, No 1 (2018): Juni
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jmpm.2118

Abstract

 This paper examines the spectrum and envelope spectrum for detection of ball bearing fault using vibration signals taken from a prototype of industrial fan. It presents a procedure to detect fault on the outer and inner race of roller bearing. Three conditions of ball bearing i.e., normal, outer race fault and inner race fault is tested. The artificial fault on the bearing is 2 mm depth and 0,7 mm width. The vibration signals are taken by using an accelerometer mounted on the vertical-radial direction of bearing pillow block. The waveforms are then transformed into spectrum and envelope spectrum. The differences between spectrum and envelope spectrum for both types of faults are analyzed and discussed. The paper explains the effect of amplitude modulation (AM) on the inner race fault to the spectrum and envelope spectrum. The discussion about the ability of spectrum and envelope spectrum for bearing fault detection is presented and compared in detail. The results show that spectrum has good ability for fault detection since it gives clear and high amplitude of bearing fault frequencies. However the spectrum often fails when the size of fault is relatively small, in this case the envelope spectrum gives better results.
Vibration-Based Discriminant Analysis for Pipeline Leaks Detection Berli Paripurna Kamiel; Indra Rukmana
JMPM (Jurnal Material dan Proses Manufaktur) Vol 6, No 2 (2022): Desember
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jmpm.v6i2.16185

Abstract

Pipelines are useful for transporting liquids from one place to another. The main problem that often occurs in pipelines is leakage which results in production and financial losses. The importance of detecting pipeline leaks makes the industries look for effective detection methods to avoid bigger losses. Several previous studies have proven that the vibration-based method is successful in detecting leaks in pipelines. However, the vibration-based method used in the previous study is relatively complicated and requires specialists to interpret the results. This study proposes a machine learning-based detection method that can classify pipe conditions directly without the help of a specialist. The proposed method is vibration-based discriminant analysis; a machine learning algorithm that recognizes pipeline conditions from their vibration pattern instead of spectrum. The proposed method was tested on a test rig consisting of a closed-loop pipeline equipped with a leak-pipe test segment. The vibration signal is taken using an accelerometer placed on the leak-pipe test segment. Time domain vibration data is extracted using several statistical parameters which aims to reveal information related to pipe conditions. The vibration data collected is divided into two groups, namely training-data and testing-data. The discriminant analysis model is trained to recognize the vibration pattern of the pipeline using training-data and then tested using testing-data. There are four leak sizes introduced in this study, small, medium, and large. Meanwhile, normal condition (no leaks) is used as benchmarking. The study shows that the proposed method is effective in classifying four pipe conditions with the accuracy up to 95%.
Co-Authors Adib Muhammad Nuh Bambang Riyanta Dimas Putro Nugroho Endah Saptutyningsih Ferry Armansyah Henry Ardian Irianta Indra Rukmana Krisdiyanto, Krisdiyanto Sudarisman . Yusuf Ahmad