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
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
In recent years, the number of human-induced earthquakes in Groningen, a large gas field in the north of the Netherlands, has increased. The majority of the buildings are built by using unreinforced masonry (URM), most of which consists of cavity (i.e. two-leaf) walls, and were not designed to withstand earthquakes. Efforts to define, test and standardize the metal ties, which do play an important role, are valuable also from the wider construction industry point of view. The presented study exhibits findings on the behavior of the metal tie connections between the masonry leaves often used in Dutch construction practice, but also elsewhere around the world. An experimental campaign has been carried out at Delft University of Technology to provide a complete characterization of the axial behavior of traditional connections in cavity walls. A large number of variations was considered in this research: two embedment lengths, four pre-compression levels, two different tie geometries, and five different testing protocols, including monotonic and cyclic loading. The experimental results showed that the capacity of the connection was strongly influenced by the embedment length and the geometry of the tie, whereas the applied pre-compression and the loading rate did not have a significant influence.
