Data Mining in MRO
Raak MKB
ProjectThe project “Data Mining in MRO” will be executed by the Aviation Academy of the Amsterdam University of Applied Sciences with a wide range of partners, including universities and companies. The project will last from October 2016 to March 2019 and be financed through a RAAK MKB grant of the Dutch Ministry of Education.
This RAAK MKB project aims to help MRO SMEs in the aviation industry to improve their maintenance process by developing new knowledge of - and a method for - data mining. Firstly the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. Finally, in several case studies these products are tested and further improved.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process.
To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry.
The approach in this project is based on the Cross Industry Standard Process for Data Mining methodology, commonly known by its acronym CRISP-DM. It is a data mining process model that describes commonly used approaches by data mining experts to tackle problems.
The research part of this project is divided into 7 relatively self-contained work packages which are loosely based on the CRISP-DM methodology and its steps:
- Understanding the maintenance business
- Understanding the maintenance data
- Data preparation
- Modelling and predictive statistics
- Development of MRO data mining tool
- Testing of MRO data mining tool
- Finalizing and implementing products
The work packages can be executed partly parallel in time, but there also exists sequential interdependencies between many of the tasks. Using CRISP-DM – a generic roadmap for data mining activities – as the structure for this research proposal, a form of design based research is applied: solutions per step are tested, developed and will finally result in a ‘data mining roadmap’ and applications specifically designed for MRO process data mining.
The partners who will contribute to the project are: Netherlands Aerospace Group (NAG), JetSupport BV, NEDAERO, Exsyn, Novulo, JetNetherlands, KVE, Flying Service (BE), Tec4Jets, ABS Jets (CZ), CHC Helicopters, Royal Netherlands Air Force (RNLAF), Delft University of Technology.
The research team consists out of:
- M. Pelt, MSc.
- A. Apostolidis, PhD
- R.J. de Boer, PhD
- M. Borst, MSc
- J. Broodbakker, BSc.
- R. Jansen, BSc.
- L. Helwani, BSc.
- R. Felix Patron, PhD
- K. Stamoulis, PhD
If you would like to participate in this research and/or would like to have more information:
Please contact Maurice Pelt at m.m.j.m.pelt@hva.nl.
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