Author:
Golovan A,Honcharuk I,Deli O,Kostenko O,Nykyforov Y
Abstract
Abstract
Remote condition monitoring of water vehicles plays an important role in preventing potentially very expensive marine incidents and ensuring maximum efficiency of a ship's operation and reliability with minimum maintenance downtime and repair costs. Concept of the condition-based approach to maintenance is today's best practise, and it is becoming increasingly important to move from planned maintenance to condition-based maintenance, to reduce the increasingly high cost of maintaining a modern fleet. Onboard and remote monitoring is now an essential part of condition-based maintenance process to obtain the good quality data, correct analysis, and effective counteractive actions necessary for such an approach, and article presents the water vehicle power plant monitoring model developed by authors. Considered approach, coupled with preventive maintenance, saves shipowners time and money through early diagnosis of component failure or excess wear. Power plant of water vehicle comprises far more than just an engine with its auxiliary equipment but also other main propulsion blocks – in particular, thrusters. The result was the development of the Water Vehicle Condition Monitoring (WVCM) system, which enables to closely examine water vehicle equipment performance. A WVCM system comprises the following installed onboard: accelerometers, pressure and temperature transmitters, oil, fuel and exhaust monitoring units and a torque measurement system.
Reference27 articles.
1. Aspects of remote monitoring of the transport vessel under operating conditions;Golovan,2020
2. Development of a diagnostic tool for condition monitoring of rotating machinery;Lin;ICOMS Asset Management Conference Proceedings,2011
3. Advanced Ship Systems Condition Monitoring for Enhanced Inspection, Maintenance and Decision Making in Ship Operations;Lazakis;Transportation Research Procedia,2016
4. Ship Machinery Condition Monitoring Using Performance Data Through Supervised Learning;Gkerekos;Int. Conf. on Smart Ship Technology,2017
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