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SCIENCE TECH: Jurnal Ilmiah Ilmu Pengetahuan dan Teknologi
Published by Universitas Sarjanawiyata Tamansiswa
ISSN : 24606286     EISSN : 25793624     DOI : 10.30738/science tech.v2i1.396
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Engineering Materials Science & Nanotechnology
Jurnal SCIENCE TECH adalah jurnal yang diterbitkan oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Sarjanawiyata Tamansiswa (UST) Yogyakarta. Jurnal ini diharapkan sebagai media bagi dosen, peneliti, praktisi, mahasiswa dan masyarakat luas yang memiliki perhatian terhadap bidang dan perkembangan ilnu pengetahuan dan teknologi. Jurnal SCIENCE TECH terbit dua kali setahun yaitu Januari dan Agustus, setiap terbit 8 artikel.
Arjuna Subject : Umum - Umum
Articles 1 Documents
 1 
Search results for , issue "Vol 12 No 1 (2026): (In Press)" : 1 Documents clear
Optimalisasi Pemeliharaan untuk Meningkatkan Avaibiliity pada Crane Barge Offshore Faisal Rahman
Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi Vol 12 No 1 (2026): (In Press)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30738/st.vol12.no1.a20573

Abstract

The effectiveness of the maintenance strategy highly influences the availability of the crane barge offshore system. This study aims to quantitatively optimize the availability of the crane barge offshore system by identifying failure patterns, determining critical subsystems that contribute most to downtime, and formulating an optimal maintenance strategy through the integration of Reliability-Centered Maintenance (RCM), Failure Mode and Effects Analysis (FMEA), and Pareto analysis combined with probabilistic distribution approaches. Data were obtained from 14 failure events over six months, covering time between failures (TBF) and time to repair (TTR). The analysis revealed that the Hydraulic System and Running Gear subsystems are the dominant contributors to downtime. The Weibull distribution was selected to model the failures, yielding a Mean Time Between Failures (MTBF) of 267.53 hours and a Mean Time to Repair (MTTR) of 7.19 hours. Simulation results showed that system availability could reach 97.66% with maintenance based on optimal time intervals. Dynamic reliability calculations demonstrated significant differences compared to conventional average-based methods, highlighting the importance of statistical approaches in determining preventive maintenance intervals. This approach provides a quantitative foundation for developing adaptive, proactive, and data-driven maintenance strategies, with broad potential applications for similar industrial equipment.

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