Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation - Zschech, Patrick; Bernien, Jonas; Heinrich, Kai (Proceedings of the 40th International Conference on Information Systems, 2019)
The availability of datasets for analytical solution development is a common bottleneck in data-driven predictive maintenance. Therefore, novel solutions are mostly based on synthetic benchmarking examples, such as NASA’s C-MAPSS datasets, where researchers from various disciplines like artificial intelligence and statistics apply and test their methodical approaches. The majority of studies, however, only evaluate the overall solution against a final prediction score, where we argue that a more fine-grained consideration is required distinguishing between detailed method components to measure their particular impact along the prognostic development process. To address this issue, we first conduct a literature review resulting in more than one hundred studies using the C-MAPSS datasets. Subsequently, we apply a taxonomy approach to receive dimensions and characteristics that decompose complex analytical solutions into more manageable components. The result is a first draft of a systematic benchmarking framework as a more comparable basis for future development and evaluation purposes.<br
Keywords: Data Science; Maintenance; Evaluation Framework; Review; Prognostics; Taxonomy