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Marine Engineeing and Offshore Technology Shipping Technology

The art of digital maintenance

The use of predictive artificial intelligence

Valletta Harbour Malta

The drive for cost savings and efficiency improvements is an important goal to operators in a marine industry that is still reeling from a downturn in the market. Since the advent of the digital era, many suppliers have expounded that shipowners need to invest in digital systems, data analytics and machine learning if they are serious about being key players in the marine industry in the future.

There is a range of software solutions coming onto the market that enables more collaboration between departments and moves the industry toward autonomous ships. But digitization and the use of machine learning and artificial intelligence (AI) is not something that will happen overnight. Instead it is a step-by-step process, as the industry learns and adapts to working with the new technology. Krishna Uppuluri, vice president of digital products at GE’s Marine Solutions, believes that there are certain aspects to machine learning and AI that need to be improved before it can be fully implemented in the maritime industry. “The marine industry has been lagging in digitization,” says Uppuluri. “But AI will have to earn its stripes as well – and this will mean a step-by-step introduction to the marine industry – first through non-critical systems, then to redundant systems, and finally to the critical ones.”

Algorithmic predictions
Companies such as Rolls-Royce have also been working on AI as part of developing autonomous ships. The company believes that through machine learning and better data-driven optimization, AI will not just save costs in maintenance, but also the time spent maintaining vessels. Machine learning is a set of algorithms, tools and techniques that mimic human learning behavior to solve problems. Rolls-Royce is using machine learning algorithms to analyze data from currently operational marine equipment and is training software models that can recognize unknown patterns in the data and make a prognosis about how that equipment is performing. Kevin Daffey, director of engineering and technology, commercial marine, Rolls-Royce, says, “If the data we analyze is ‘big’, then the model can recognize more complex patterns and make more accurate predictions about the state of the marine equipment than any human could.” Potentially this means maintenance in the future could be carried out in a more timely and cost-effective way and could further improve the reliability of equipment.

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