Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation
Zschech P, Bernien J, Heinrich K (2019)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2019
Publisher: Association for Information Systems
Conference Proceedings Title: Proceedings of the 40th International Conference on Information Systems
Event location: München
ISBN: 9780996683197
URI: https://aisel.aisnet.org/icis2019/data_science/data_science/4/
Abstract
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.
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APA:
Zschech, P., Bernien, J., & Heinrich, K. (2019). Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation. In Proceedings of the 40th International Conference on Information Systems. München, DE: Association for Information Systems.
MLA:
Zschech, Patrick, Jonas Bernien, and Kai Heinrich. "Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA’s Turbofan Degradation." Proceedings of the 40th International Conference on Information Systems (ICIS), München Association for Information Systems, 2019.
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