Researchers at the University of Dubrovnik have made significant strides in enhancing the reliability of ship cranes through a new continuous simulation model designed to predict failures. Led by Mate Jurjević from the Maritime Department, this innovative approach aims to minimize unplanned downtime during crucial cargo loading and unloading operations, which can often lead to costly delays and operational inefficiencies.
The study utilizes a comprehensive database from the GALIOT software package, which has been used for crane maintenance over 120,000 working hours. By employing fault tree analysis (FTA) to identify the root causes of failures and a Markov mathematical model to simulate the various states of crane operations, the researchers have developed a robust system dynamics simulation model. This model not only predicts potential failures in hydraulic motors and pumps but also estimates the frequency of these failures, thereby allowing operators to plan maintenance more effectively.
Jurjević states, “The simulation model shows high reliability of the cranes and a constant frequency of failures throughout the 120,000 operating hours.” This consistency in failure rates offers crane operators a clearer understanding of maintenance needs and scheduling, which is critical for minimizing disruptions in operations.
The commercial implications of this research are substantial. For shipping companies and port operators, the ability to predict crane failures can lead to significant cost savings by reducing downtime and maintenance expenses. Furthermore, the model’s predictive capabilities could enhance operational efficiency, ensuring that cargo operations run smoothly without unexpected interruptions.
The study emphasizes the importance of timely maintenance and monitoring, particularly in the challenging environments where ship cranes operate. The findings suggest that by adopting this simulation model, companies can improve their maintenance strategies and ultimately enhance the safety and efficiency of their operations.
As the research highlights, the potential for broader application exists. Jurjević notes, “The recommendation is to research other types of cranes under different operating conditions and environmental factors.” This opens up opportunities for further development of the model across various sectors that rely on heavy machinery and equipment, potentially transforming maintenance practices industry-wide.
This groundbreaking research was published in the Journal of Marine Science and Engineering, underscoring its relevance to the maritime industry and the ongoing efforts to improve the reliability and efficiency of ship cranes.
