As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementations
MULTIFILE
This book is both a short introduction to the recent developments, challenges and opportunities in Aviation Maintenance, Repair and Overhaul(MRO), and at the same time, a presentation of the research focal areas and the key waypoints towards smarter and more sustainable MRO. Innovation and integration have always been key aspects of Aviation. Currently, evolutions in aircraft design, materials and production techniques are ahead of the MRO practices in use.This gap is creating demand for new knowledge to develop and operationalise adaptive, digital and sustainable MRO tools, applicable or integrated in modern aircraft systems and components.
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. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, 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. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
Service logistics in de vliegtuigonderhoudindustrie is een zeer kennisintensieve en concurrerende markt. De meest cruciale factor in deze industrie is het laag houden van de downtime tijdens maintenance, repair en overhaulactiviteiten. Met name opslag, distributie en het managen van spare parts spelen hierin een belangrijke rol. Om tijdig vliegtuigen te kunnen onderhouden, hebben onderhoudsbedrijven vaak duizenden onderdelen, van kleine ophangpennen tot zeer dure motoren, op voorraad. Hierin zit dan ook de paradox: onderhoudskosten dalen door lagere down time en grote voorraden zorgen op hun beurt weer voor hoge warehousing kosten. Het lectoraat Aviation Engineering voert thans een RAAK-MKB project uit waarin primair wordt onderzocht of historische onderhoudsdata kan worden gebruikt voor MRO-onderhoudsplannen die de downtime verlagen. Gaandeweg de uitvoering van dit project is echter gebleken dat niet alle onderhoud van te voren gepland kan worden en dat juist real time data tijdens de vlucht erg relevant is voor snel en efficiënt onderhoud. De doelstelling van dit KIEM-project is enerzijds het vergaren van nieuwe kennis en inzichten over service logistics en het daarmee aanjagen nieuw onderzoek waarin wordt onderzocht of de inzet van real time big data bijdraagt aan het verminderen van de downtime. Anderzijds wordt onderzocht of nieuwe samenwerkingen (met IT-bedrijven) mogelijk zijn die voorraadkosten verminderen. Onderzoek wordt gedaan naar: 1. Knelpunten voor de inzet van real time big data in relatie tot MRO-activiteiten. 2. Vraagarticulatie en samenwerkingsmogeljkheden met nieuwe mkb-bedrijven. 3. Spare part warehousing efficiëntie (parts pooling). 4. Infrastructuur en standaarden voor opslag en toegankelijkheid van gezamenlijke en individuele (bedrijfsgevoelige) data. De HvA, NAG en JetSupport verwachten dat met dit project nieuwe mkb-onderhoudsbedrijven, vliegtuigmaatschappijen en overheden gaandeweg het project gaan aanhaken. Uitkomsten zijn enerzijds nieuwe kennis en inzichten op het gebied van service logistics en anderzijds commitment voor een vervolgonderzoek op het lopende RAAK-project.
