Predictive maintenance is making its way into the aviation industry and along with it comes a series of prognostics and health management solutions (PHMs) that can unlock a number of benefits for airlines and MROs. Where lies the true value and what are the key considerations? Find out here.
A shift is happening in aircraft maintenance. An increasing number of MROs and airlines seem to be moving from a preventive maintenance approach to a predictive maintenance approach, fueled by data and analytics.
The idea behind predictive maintenance is to use data and analytics to generate predictions of when aircraft parts are going to fail and replace them before they do.
Successfully doing so has already proven its worth, with several cases of airline operators who have witnessed reductions in unscheduled maintenance, fewer service interruptions and optimized shop visits.
A group of key players are leading the way with regard to utilizing data in aircraft maintenance, while the rest of the industry sits tight and depends on the experiences that these
So what are the first experiences with the predictive and actionable analytics derived from the new prognostics and health management solutions? Which factors must be considered when evaluating the value of PHM for airlines?
In this article, we bring you the key insights from some of the industry’s leading data and analytics experts who sat down to discuss the prospects of predictive maintenance at MRO Europe 2018.
Lufthansa Technik Group is one of the MROs leading the way when it comes to PHM-solutions and predictive maintenance, and although the technology is still in the early phases, they are already seeing results. According to the MRO’s Senior Director of Analytics and Data Solutions, Jan Stoevesand, there are two areas where predictions can help.
»Predictions will always help safety—but in regards to cost reduction, we have identified two areas where predictions can make a tremendous difference: These are reducing MRO costs and reducing operational instances,« he explains and continues:
»There’s one problem with this. We’ve been working heavily with reduction of MRO costs for the last 40 years, so the potential savings in this area is quite small. The real savings are in the avoidance of operational instances. That’s where the money is. However, you really need a set of KPIs to be able to tell if all the investments into predictive maintenance really work – so I think the KPIs are just as important a success criteria as the PHM-solution itself.«
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Wouter Kalfsbeek, E&M Big Data Lead at KLM, agrees and believes that the potential benefits of predictions will be much greater in the future when systems are connected across the supply chain. This movement towards ultimate connectivity is also known as Internet of Things, in short IoT.
»If we are able to extend to a larger proportion of aircraft systems in the coming years, we’ll be able to tie more closely with the airline operations, and then you can really make massive cuts on the ground time and disruption times. I think this is when it really gets exciting – when everything becomes connected. We’re just in the beginning now, with small systems working independently, but potentially we could transform all maintenance intervals so they are based on data and thus stop doing conditional checks. It’s complex, but I think we will get there,« Wouter Kalfsbeek explains.
In the near future, it seems that PHMs will not only generate value for airlines, but for the whole supply chain – as well as the passengers themselves. Brandon Keener, a Senior Director at UTC Aerospace Systems, who is representing the suppliers of PHM-solutions in the debate, explains:
»Making predictions for an airline is directly tied to logistics and the whole supply chain. In a perfect world, the aftermarket supplier will be able to see when customers need a spare, and in this way, the supply chain is getting much more sophisticated by having this call in advance warning 3-4 weeks out. This extends all the way down to the OEM side – an aftermarket or repair shop is, after all, only as good as the place they can get their OE components. So the more visibility you can put into the system with predictive analytics, the more time you give everyone to prepare.«
The end customers will also benefit, as airlines are able to reduce costs and increase safety.
»We certainly see predictive analytics directly benefiting the passenger experience. Ultimately, the benefits will translate into lower airfare costs and fewer delays,« says Brandon Keener.
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Traditionally, failures don’t have a positive impact on businesses but in the case of data analytics and machine learning, failures are key. This is what drives and improves the accuracy of algorithms. Without a constant flow of new observations, the PHM-solutions will never reach their full potential.
There are two important aspects to consider when evaluating the success of PHMs.
»As I already said, a stable KPI system is really important, in order to prove that the reliability goals are met. Secondly, it’s critical to ensure that the people who do the analytics are connected to the MRO or shop people. You have to constantly improve the algorithm so they continue to stay accurate, and the people on the floor are crucial in this matter. They need to look at the parts on which the system has predicted a failure and investigate if the predictions are actually correct. Then they must report back to the data team who can feed this new information into the system. It’s so important to establish this constant feedback loop in the organisation,« Jan Stoevesand from Lufthansa Technik Group explains.
Wouter Kalfsbeek from KLMs data team agrees.
»Failures are good. They help develop better prognostics and it’s really important that we learn from these mistakes. If you come to a point with too few failures to train the models, it will impact the performance of the system. I think this is why we could all benefit from sharing these failures with each other because everyone will be able to develop and use better algorithms,« he says.
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This leads us to another vital part of the discussion; sharing of the data. Part failures within a typical fleet usually don’t happen by their thousands, so gathering enough data to make accurate computer models can be a real issue in the aviation industry. That’s why a lot of the professionals working with predictive maintenance and data analytics are calling for collaboration in this area.
»As an industry, we need to separate data acquisition from data usage. We have to build a Chinese wall between these two things. We should collaborate on data acquisition and then compete on data usage – it just makes so much more sense for all of us,« says Jan Stoevesand.
Wouter Kalfsbeek from KLM also believes data collaboration is the way forward, but he also raises an important issue that would rise alongside a potential democratization of data.
»We all have data and are able to generate value for ourselves. If we put it together, we can generate even more value, but how do you divide that value so everyone benefits more or less equally? That’s the question we haven’t really figured out yet,« he explains.
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But who really owns the data from aircraft? Is it the operators? The aircraft manufacturers? The panel widely agrees that it should be the operators who own the data, but there’s no clear standard for this in the industry.
»Data ownership is another important discussion. I think we all need to agree that the aircraft operators are the owners of the data. At Lufthansa, we developed a simple model for this, and we call it the three C’s. Control, choice and competition,« says Jan Stoevesand and continues:
»It means that the operator of the aircraft controls the data and gets it encrypted. They can choose what to do with the data and whom to share it with. This will lead to healthy competition amongst the companies that are able to turn data into insights. This competition is good – it will help us and the industry as a whole to develop better prognostic solutions.«
The growing popularity of predictive maintenance has created an influx of PHM-solutions coming into the market. As an airline or MRO, choosing the right solution among the many offerings and suppliers can be a make or break factor, but how do you determine whether or not the solution fulfils your needs?
»I think the most important question to ask yourself when evaluating a PHM-solution is to identify whether or not the product ends with the prediction or with the fulfilment. Find out where the product connects with the physical world. This is where you start to save money.
Everything before this, such as getting the data, cleaning the data, doing analytics and generating predictions will cost you money, it’s the last step that will save you money,« says Jan Stoevesand, Senior Director Analytics and Data Solutions at Lufthansa Technik Group.
Brandon Keener, the Senior Director at UTC Aerospace Systems recognises the huge influx of new suppliers in the recent years.
»As a supplier, it’s honestly a bit of a wild west out there. There’s a huge spectrum of suppliers offering different things to different operators to fulfil their needs. Some promise that they can support whole fleets with their analytics and proprietary algorithms, others provide a laser-like focus on solving a particular problem,« he says and adds:
»At UTC Aerospace Systems, we think there are three success criteria to a truly valuable PHM-solution: The design, the team that has to build and develop the analytics, and lastly whether or not these have access to the return data from the MRO. We certainly see other analytics or solutions that give the customers what they want without ticking all three criteria, but from our perspective, this is the “secret sauce”.«
In these early stages of PHMs, harvesting rewards aren’t necessarily about how much you are willing to invest in a complex algorithm. The extensive knowledge that the engineers have is an excellent place to start when developing computer models, notes Jan Stoevesand from Lufthansa who gets the last word in the debate:
»The complexity of the algorithm doesn’t correlate with the savings. Extremely complex algorithms are not necessary to save money. If you are starting out with a predictive prognosis program, you can gain a lot of value by focusing on the knowledge the engineers already have. This knowledge is very stable as it’s derived from the physical world with years of testing. Of course, we need to combine this with machine learning, but we shouldn’t forget to take advantage of the resources we already have.«
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