Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly being integrated into our everyday lives. These innovations are a result of understanding the power of data and how vast volumes of data provide the opportunity to create smarter devices, systems, and machines.
It might come across as sci-fi, but you’ve likely already come across some recently, such as Google Maps, email spam filters (which use ML to detect keywords or metadata), and freeway toll cameras that use AI to identify vehicle license plates.
A yacht is a data-generating machine. Shore-power units, air-conditioning plants, sewage treatment plants, and refrigeration systems are all in continual use at the dock and producing data on how the system is running. Running logs are a great example of how today’s engineers use data to help detect issues prior to them occurring by performing trend analysis and noting any spikes or dips in running temps, pressures, or levels.
So how will AI and ML shape how engineering is carried out on board superyachts? The most likely early technology to become more common is condition-based maintenance or predictive maintenance. This relies on ML to track component data while in operation to detect if maintenance is required.
Put simply, an engineer may clean the sea strainer for the air-conditioning seawater system on a fixed time-based interval. Condition-based maintenance can use sensors and flow meters to detect if the flow within the system is outside of an acceptable range and therefore notifying the engineer to clean the sea strainer. The effect of this maintenance approach is greater efficiency. When completing tasks that require consuming spares, it can significantly reduce operating costs and limit downtime caused by unexpected breakdowns. The process of troubleshooting could be the next frontier benefitting from AI and ML technology in the engine room.
The Hyundai Motor Group launched the world’s first AI-based diagnostics fault detection system that utilized the engine sound recordings to detect possible powertrain issues. The process involves recording the engine noise via fixed microphones, analyzing the sound signatures, extracting the specific vectors, then using AI to compare against a database of known faults.
This functionality could be applied to any component that creates sound when it’s operational on board a superyacht. Main engines, generators, gear boxes, pumps, compressors — all ideal candidates for this technology.
In 20 years, the world of yacht engineering will value and utilize data far better. Similar to the leap from analogue monitoring systems to more electronic monitoring systems, the next leap will be as influential.
This article originally ran in the March 2022 issue of Dockwalk.