Artificial Intelligence (AI) in Condition Monitoring for Land, Sea & Air Transport
When it comes to the maintenance of engines and other mechanical equipment, there are lots of different things that can go wrong. But with the right systems in place, the potential for costly failures and repairs can be seriously reduced. Systems like our very own MachineCare + can massively reduce failures in mechanical components used on the land, in the sea and in the air all thanks to specially created AI.
What Is Condition Monitoring?
Before we get into what can benefit from AI-powered condition monitoring, it’s important to understand exactly what condition monitoring is. Condition monitoring is the process of ensuring everything that keeps a machine running, is working effectively. This can mean anything from oil and fuel to coolants and individual parts, because if one element breaks or doesn’t work the whole machine can grind to a halt, quite literally. You can find out more about AI Condition Monitoring and in particular our MachineCare + software here.
The first thing that comes to mind when you mention engines on land is normally cars. When it comes to the maintenance of cars, HGV’s or busses we often rely on the internal computers to diagnose any faults. But the problem with these systems is they can only diagnose faults once they happen which means you already have a potentially costly repair on your hands and time when you can’t use the vehicle because it’s being repaired. This is an especially important consideration for companies operating a fleet of vehicles. Running the oil, fuel and coolant through an analysis system can reveal a lot more information about the health, efficiency and degradation of essential components. Spotting these issues early prevents them from escalating further to a more serious, or more costly issue.
Modern boating has also seen a big increase in the number of mechanical parts. Similar to land vehicles, moving parts can break down over time which can go undetected until it’s too late. But this degradation can be detected by analysing the fluids that keep it moving, like oil and coolant. As parts become worn or damaged, fragments end up in these fluids that can be detected using advanced software and AI to not only fix the problem before it’s a problem but optimising the components to prevent it happening again. But it’s not just engines that can suffer the effects of bad maintenance. In recent years, with a big focus on more environmentally friendly energy sources, tidal and wave power have become rampant. But, for these systems to work all of their moving parts need to be looked after. By analysing the lubricants used in these generators, you can find any sort of external contaminant or signs of excessive wear in the moving parts and prevent unnecessary downtime. Thanks to our specialist AI, these minute particles can be detected quickly and easily and with input from experienced engineers, a solution to solve and prevent the issue can be reached.
Ensuring that all the mechanical parts and systems are functioning correctly and optimally is perhaps most important for air travel. Any kind of engine failure in an aircraft can have catastrophic consequences. From planes to helicopters to drones, they will need maintenance of some description just as they all need some kind of lubrication to keep parts moving and prevent the whole system ceasing up. Whilst there might be less risk of an external contaminate like dirt or soil entering the lubricant compared to land vehicles, particulates created from overworn or broken components can still cause and signify problems. But it’s not just planes and helicopters that can benefit from AI-powered condition monitoring, wind turbines are amongst the moving parts that require servicing and maintenance. Without servicing, their massive blades will simply cease up, meaning they can’t generate any electricity which would leave millions of homes without power. Hence why it is vital that downtime for wind turbines needs to be minimal, something only achievable through condition monitoring.