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All Journal International Journal of Marine Engineering Innovation and Research
Egas Wahyu Endrianto
Departement of Marine Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, Indonesia.
Automotive Engineering Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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System Modeling of Predictive Maintenance for Engine Health Monitoring on Ship Auxiliary Engines Using Vibration Variables Egas Wahyu Endrianto; Indra Ranu Kusuma
International Journal of Marine Engineering Innovation and Research Vol. 10 No. 3 (2025)
Publisher : Department of Marine Engineering, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25481479.v10i3

Abstract

This research presents a dynamic system modelling approach for predictive maintenance of ship auxiliary engines using vibration variables. The model integrates key mechanical components—piston, crankshaft, camshaft, valve train, and timing gear—based on the specifications of a Yanmar TF85 diesel engine. Each subsystem is modelled using a multi-degree-of-freedom (MDOF) state-space framework to represent vibrational and structural dynamics. Simulations are carried out in MATLAB/Simulink under various engine operating conditions, including normal operation at high, medium, and low RPMs, as well as fault scenarios such as damping degradation in the piston and crankshaft. A fuzzy logic system is employed to interpret the vibration data and determine the impact level for each condition. The results indicate that under normal conditions, the engine maintains stable vibration levels, while faults lead to significant increases in velocity RMS values and impact severity. Disturbances in the piston result in dominant amplitude changes, while crankshaft faults affect the frequency propagation throughout the system. These findings confirm that the proposed model can effectively detect early mechanical deviations and support the implementation of predictive maintenance strategies for marine diesel engines.
Co-Authors Indra Ranu Kusuma