Evaluating the PHM-Solutions on the Market

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 of 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 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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The impact of PHM solutions