Abstract
Predictive maintenance is gradually replacing conventional preventive maintenance, through an informed decision-making process for a fleet operator to proactively monitor the health status of the sea-based equipment and machinery. As a result, the fleet operator will only need to ask for on-time repair and maintenance services instead of periodic maintenance, which saves both time and cost significantly. While moving into this new maritime business model, which is also known as Service-as-a-Product (SaaP), both fleet operators and maintenance, repair, and overhaul (MRO) companies could also build a stronger collaboration in the maritime industries. With the advent of digital transformation towards Industry 4.0 (I4.0), the Industrial Internet-of-Things (IIoT) renders the massive collection of operational and machinery process data from the vessel equipment through sensorization, where these collected real-time information are useful for advanced analytics to predict equipment failure and to avoid unplanned downtime. Unlike other industries, there are a few challenges in the maritime industries when developing a centralized smart vessel equipment monitoring platform. One of the key challenges, is the lack of a feasible data management system, requires the centralized host must be able to handle and manage the telemetry IoT data that is transmitted over the satellite communication, by complying with all cybersecurity considerations and regulations in maritime sector. In this paper, we present a scalable, hybrid cloud-based data management framework that can connect to multiple edge systems where each system is deployed on a physical vessel, to establish a scalable SaaP business model. Hence, the ship-to-shore sensorized data can be processed and monitored via a real-time visualization dashboard through the centralized cloud-based platform. We argue that our approach, would be a turn-key solution that can be implemented for mostly all types of marine equipment and machinery, and thus to further improve the prediction tools to support advanced decision-making techniques, such as optimal time repair of vessel equipment.
Published Version
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