Abstract
Data sets grow rapidly, driven by increasing storage capacities as well as by the wish to equip more and more devices with sensors and connectivity. In mechanical engineering Big Data offers new possibilities to gain knowledge from existing data for product design, manufacturing, maintenance and failure prevention. Typical interests when analyzing Big Data are the identification of clusters, the assignment to classes or the development of regression models for prediction. This paper assesses various Big Data approaches and chooses adequate clustering and classification solutions for a data set of simulated jet engine signals and life spans. These solutions include k-means clustering, linear discriminant analysis and neural networks. MATLAB is chosen as the programming environment for implementation because of its dissemination in engineering disciplines. The suitability of MATLAB as a tool for Big Data analysis is to be evaluated. The results of all applied clustering and classification approaches are discussed and prospects for further adaption and transferability to other scenarios are pointed out.
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Pitz, K., Anderl, R. (2019). Implementing Clustering and Classification Approaches for Big Data with MATLAB. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol 880. Springer, Cham. https://doi.org/10.1007/978-3-030-02686-8_35
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