RAMS and performability techniques are used in many engineering fields to design and operate industrial assets to meet safety standards and to optimize overall system performance. While these techniques have proven value in system design, their application in operations has been lacking, although they offer a valuable approach for evaluating and comparing different scenarios from a risk-based perspective. However, in the next decade a new set of RAMS techniques that leverage the use of (near) real-time monitoring of operational parameters will increasingly be used by the shipping industry.
The most immediate expected benefits of these types of real-time analytics in supporting Asset and Operation Management will be to enable owners to reduce the number and frequency of inspections and repairs, and allow them to anticipate and replace damaged and worn parts with minimal resources and downtime. Similarly, these systems can map a ship’s condition status in relation to safety risk levels, allowing for dynamic adjustment of safety barriers in order to maintain minimum safety levels. With real-time access to a vessel’s current and future status, maintenance and operational personnel will have more accurate information on system capabilities, allowing for timely action to increase reliability, availability, safety, and efficiency.
In order to achieve the full potential of real-time analytics, further development of a number of technologies is necessary. The performance of real-time analytics is a function of predictive data that can indicate a developing failure. Therefore, smart sensor networks will be critical, as their ability to work together offers a detailed and accurate picture of various systems. In turn, real-time analytics will rely not only on how sensors are configured and linked, but also on the quality of ship-to-shore connectivity. Due to limited onboard storage and processing power, data will be analysed on board and/ or sent to shore, where it will be managed by increasingly sophisticated software tools and computing power. These tools will provide full-range analytics and visualization capabilities, and be seamlessly linked to onboard sensor and actuation devices via the Internet.
Advances in how organizations run their work processes following a data-driven approach will enable a dramatic shift in how the industry approaches asset management. As an example, this could involve moving from a scheduled maintenance approach, a process that is often driven by supplier recommendations, to condition-based maintenance, driven by the actual condition of onboard components and systems. This shift alone may require a new type of agreement between service providers and vessel owners and operators, perhaps through agreed levels of performance that are measurable at any time.
Data quality will represent a critical factor for the successful implementation of real-time analytics. The adoption of a data-driven philosophy for asset operations, such as reliability-centred maintenance, lifecycle asset management, and system engineering, will therefore become even more important in the maritime industry. Furthermore, new standards to verify the quality of real-time data streams will need to be developed. Similarly, the ability to trust data analytics and black box models will also need to be demonstrated, and new formal approaches for analytics verification must be developed. As more stakeholders will rely on information retrieved from several sources, it will be necessary to guarantee consistency across industry stakeholders’ data lakes and information models. Last, but not least, for a full industry-wide implementation, new standard and technological solutions for data governance and cybersecurity will be needed.