Blog Archives

We are Carbon Neutral

Climate change is one of the most pressing problems facing our world today and the urgency of the climate crisis requires swift and ambitious action.

At the time of the COP26 Climate Conference in Glasgow and the significant importance it represents to the world, we are proud to announce that we are now Carbon Neutral.

Being carbon neutral means that we have calculated how much carbon we emit as a business (our ‘carbon footprint’), and have reduced this to zero through a combination of reducing our emissions internally and supporting external carbon ‘balancing’ projects.

AES Supports Major British Army Exercise in Germany

AES Defence was requested by the Ministry of Defence to provide support to a major exercise taking place in Sennelager, Germany during January and February 2021.

The primary focus was to be able to provide the Oil Health Monitoring (OHM) service with a fast turnaround time during high intensity training.
An AES Defence Technical Service Engineer deployed to Germany during this to process the fluid and oil samples taken, from their large fleet of armoured vehicles. A sample collection and delivery process was implemented to facilitate the rapid delivery to the laboratory, and return the results directly to the maintenance and service personnel.

The AES employee was on hand to also provide guidance and OHM training to the exercising troops and this was fully taken advantage of by the Regiments involved.

There were numerous challenges to overcome in order to deliver the service fully, with the major factor being the Coronavirus pandemic and full lockdown conditions throughout Europe at the time. Although there were these extra challenges, the provision of the service was a great success with sample turnaround times being vastly reduced.

AES exhibiting at DSEI 2019

AES are currently attending the Defence & Security Equipment International (DSEI) event at ExCel London and we welcome you to visit us at stand S4-203 where we would be happy to provide you with an overview of our services.

What’s The Difference Between Neural Networks and Deep Learning

Technology and data have grown at an exponential rate over recent years. The likes of AI has seen a huge boost in new advancements. We have even created robots and AI that can not only adapt but continually learn in order for them to perform their tasks better.

But not all methods of learning and attaining data are the same – there are 2 main ways that computers can learn for themselves.

Neural Networks

Neural networks aim to mimic how the human brain carries out the process of thinking. Our brains are organised into hierarchical tiers whereby all of the information we deal with is parsed through each ‘layer’ before being sent to the next level up the chain. For example, you will go through the following sequence of the thought process for most of the information you take in on your day to day life:

  • Data input
  • Thought
  • Decision Making
  • Memory
  • Reasoning
  • Action

Whilst computers can’t replicate the complexity of this automated process just yet, they are getting there and it’s neural networks that are providing the basis of this development. In it’s simplest form, a neural network can have 3 ‘layers’: one for input, a hidden layer for data processing and the data output. Of course, they can get much more complex by adding additional hidden layers to process the information in different ways but they are limited to what they can do.

Deep Learning

Deep learning is the next step above neural networks. Deep learning is capable of learning how to teach itself what to do with new information and how to process it, whereas neural networks require to be taught how to process new data. Computers achieve this ability to be self-taught through multiple layers created within a neural network because without such a network deep learning would not be possible; in order to act like a human brain, it must first replicate its structure. Deep learning passes information through many hidden processing layers (called a deep neural network) and ‘learns’ by filtering the information out to achieve a goal. This information is then shared between the neural network and used for future reference which results in the learning. 

So whilst you might have first thought the two were interchangeable, in fact one relies on the other; without neural networks, there wouldn’t be deep learning.

The future of artificial intelligence and machine learning is going to continue to develop at incredible speeds over the next few years.

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.