Propel Yourself Above the Competition
Your sensors enable airlines to identify problems with their jet engines before they endanger passengers. This is an essential service, and your customers are happy to pay for it. But there are lots of companies out there doing what you do. How do you set yourself apart from the competition?
Poor aircraft maintenance can cost lives. It also costs your customers time and money. We all know what it’s like to hear that dreaded announcement at the airport: the flight is delayed due to a mechanical problem. You can almost hear the groans of unhappy passengers. Maybe they have a tight connection to make. Maybe they’re going to be late for an important meeting. Maybe they just can’t wait to be on the beach sipping a cocktail. Whatever the reason, delays mean frustrated passengers, which means negative publicity and loss of revenue for the airline, which means an unhappy customer for you.
A jet engine is expensive – your customers want to extend its RUL (Remaining Useful Life) as long as possible, but not so long that they risk delays, cancellations, or accidents.
What if you could use your customer’s data to predict the exact point in time when one of their jet engines is going to fail, so they could solve the problems that keep planes sitting at the gate or on the runway when they should be in the air?
Reliable Predictions = Happy Customers
Machine learning incorporates all the complex data your sensors are already collecting about types, circumstances, and characteristics of jet engine failure over time and predicts when the next engine failure will occur so your customers can take action. What’s more, machine learning is much more reliable than human inspectors when it comes to identifying problems.
Maintenance errors and negligence contribute to more than 40% of fatal airline accidents and almost 50% of delays. Your customers may have the best mechanics out there, but they’re only human. They get tired; they miss things. And there’s only so much information a human brain can assimilate. Good mechanics know how to spot the problems they’re looking for, but what about things they don’t know they should be looking for in the first place? When it comes to jet engine failure, the best prediction system is literally a matter of life and death. A machine can predict when an engine will fail much more accurately than a human being can. When you have thousands of pieces of information coming in from your sensors, it’s impossible to keep track of it all manually. Your customers need a system to predict when various types of failure are likely and to tell them when to intervene – before the problem occurs.
You’re already monitoring this data for your customers, but without an optimized machine learning process, you’re allowing them to take too many unnecessary risks. You can help your airlines increase safety, win customers, and boost revenue by making the best use of all the complex data you’re already collecting.
Fix Problems Before They Become Failures
Data Revenue’s custom AI prediction solution uses the power of machine learning to help you analyze all your customers’ data and make accurate predictions so they can fix problems before they impact their core business. And that kind of information advantage makes you the preferred service provider.
How Does It Work?
There’s never enough time for an engineer or a mechanic to make sense of all the complicated data your sensors are collecting. Machine learning works with a huge number and variety of recurrent events and patterns – much more than any human analysis can handle. And it makes flexible predictions by assimilating all that information on a timeline. Machine learning allows you to turn your big data into a better, smoother, safer process for your customers.
There are so many variables when you’re dealing with complex machinery. How can any system accurately predict all the different types of jet engine failure?
Jet engines fail in various ways, for multiple reasons; it’s not easy to come up with one simple definition of what “failure” means – but we all know the results of failure, and that’s what your customers want to avoid. If you tell Happy Jet Airlines that an important component in the engine of Aircraft 24601 is likely to fail within the next 60 days, then they have 60 days to work on it, to find and fix the problem before anything tragic happens. It’s up to them to figure out what type of failure they’re dealing with. But the fact that you can tell them there’s going to be a problem puts you leaps and bounds ahead of the competition.
My job is data-gathering; it’s up to the airline to maintain the engines. Why do I want to get involved in predicting jet engine failure?
Maintenance is up to the airline, but human beings can only assimilate so much information at a time. And they’re liable to make mistakes. Only a machine can take the huge variety of past indicators that precede the myriad kinds of jet engine failure and use all that data to accurately, objectively predict when an engine will fail in the future. Airlines know that human error is responsible for the majority of delays and accidents, but up till now they haven’t had a better way of predicting engine failures. If you could provide the data that gets the fix-it process started before it impacts core business, you could guarantee a line of customers waiting to sign up for your services.
My data-collection sensors are incredibly complex. How will machine learning affect the system I’ve already set up?
Our job is to feed optimized information back into your system, no matter which system you use. We make sure the information gets delivered exactly where you need it, when you need it. The predictions will surface as a new attribute in the same tool you’ve already been using. From there, you can use the data – and pass it on to your customers – any way you’d like.
This sounds expensive. What will a custom prediction solution cost?
We build custom AI prediction systems for about 10% of the price of building them in-house. We make sure every prediction solution pays itself back in additional revenue within six months. In fact, our average payback period is three months.
A New Standard for Predictions
Instead of predicting the chance of failure in a specific time-window, we predict a probability for every moment in the future. You will know the exact moment when the chance of failure is highest, and also see what the probability curve looks like. Our cutting-edge approach using Recurrent Neural Networks (RNNs) makes this possible.
The Perfect AI Prediction Partner
Data Revenue has a string of success stories and happy customers that testify to the ease of our custom AI processes and the benefits of our experience. Let us help give you a leg up in the market. Contact us to discuss a custom AI prediction solution today!