predictive Modelling

 
 
Machine Learning based prediction models to solve real industry challenges. It all begins with a business challenge. Prediction of aircraft component failure to prevent unscheduled downtime, prediction of to be expected findings during base maintenance to better align spare parts and ground time, prediction of incoming workload for MRO shops to align manpower requirements. All examples of use-cases in aircraft maintenance for which prediction modelling is a very real solution. We support you with custom prediction models to gain as much value as possible from data and decrease maintenance costs.
 

How we Support airlines and MRO’s
to build prediction models

 
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Find your use-case

Exploring the different business challenges together with your key people drives the need to find the right business use-case for your airline or MRO to start adopting predictive analysis.

Return on investment also needs to be considered when finding the right business use-case. Making sure that the investment into a predictive analytics project also provides business benefits that justify the investment.

 
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Collect required Aircraft Data

Define model data that is required for a specific prediction use-case.

Data collection; For prediction models to provide meaningful results, the underlying aircraft data is of crucial importance. Ranging from the aircraft airworthiness - , aircraft maintenance - and aircraft onboard data. With our technology we established a controlled data flow, collection and transforming all required data for the prediction use-case.

Ensuring data quality, next to data availability, aircraft data quality and frequency of aircraft data availability are important aspects to safeguard as part of adopting aircraft prediction models.

 
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Machine Learning modelling

Model selection; depending on the data and use-case different machine learning models might prove to gain better results for clients. We run different comparisons on the data to determine which machine learning model provides the best outcomes.

Model development, all machine learning models are developed on EXSYN’s Aircraft Data Operations Platform to ensure proper computing power, data processing and model behavior.

 
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Dashboard creation

Presenting the outcomes of prediction models in meaningful dashboards assists engineering and MCC teams in their decision-making process.

Outcomes required for dashboarding can be provided to clients through API’s for embedding in their own analytics infrastructure, or can be visualized through dashboards in EXSYN’s Aircraft Data Operations Platform.

Why choose EXSYN for your predictive project

  • Industry expertise

    Combined expertise in aircraft data, systems used, aircraft types and aircraft maintenance processes.

  • Approach

    Best-practice approach build on years of developing and implementing advanced analytics at airlines and MRO’s

  • the team

    A can do attitude with a natural drive to get the best result achievable

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Predict Aircraft Component Failure

Use advanced statistical modelling combined with feature importance in random forest to predict component failure and subsequent unscheduled ground time of aircraft.

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Get in touch

Leave your details below to learn more about EXSYN’s predictive Modelling approach, one of our specialists will get in touch with you to discuss your requirements.

hello@exsyn.com | +31 (0) 20 760 8200