Open Access
| Issue |
E3S Web Conf.
Volume 170, 2020
6th International Conference on Energy and City of the Future (EVF’2019)
|
|
|---|---|---|
| Article Number | 02007 | |
| Number of page(s) | 8 | |
| Section | Factories of Future | |
| DOI | https://doi.org/10.1051/e3sconf/202017002007 | |
| Published online | 28 May 2020 | |
- S. T. N. R. Y. Zhong, X. Xu, E. Klotz, Engineering 3(5), 616-630. (2017) [CrossRef] [Google Scholar]
- Deloitte Insights. (2019). A whitepaper on Making maintenance smarter [last accessed on September. 2019]. [Google Scholar]
- How Manufacturers Achieve Top Quartile Performance. (2017, March 6) [last accessed on September 2019]. [Google Scholar]
- Loon, R. (2019). How Industrial IOT is Influenced by Cognitive Anomaly Detection - The Digital Transformation People [last accessed 15th July. 2019]. [Google Scholar]
- Demands on Sensors for Future Servicing: Smart Sensors for Condition Monitoring by Thomas Brand, 2017 [last accessed on September 2019]. [Google Scholar]
- Choudhary, P. (2019). Introduction to Anomaly Detection, Towards Data Science [last accessed on 1st September, 2019]. [Google Scholar]
- Flovik, V. (2018). How to use machine learning for anomaly detection and condition monitoring, [last accessed 5th September 2019]. [Google Scholar]
- R. Silipo,2019 “IoT Anomaly Detection 101: Data Science to Predict the Unexpected”, Dark Reading, [last accessed: 23- Sep- 2019]. [Google Scholar]
- Z. Tang, Z. Chen, Y. Bao, and H. Li, Struct. Control Heal. Monit 26,(2019) [Google Scholar]
- X. Li, W. Zhang, Q. Ding, and J. Q. Sun, J. Intell. Manuf. 31, 433–452 (2020). [Google Scholar]
- A. Graß, C. Beecks, and J. A. C. Soto, Unsupervised Anomaly Detection in Production Lines,TIA Vol. 9(pp 18-25) Springer Berlin Heidelberg, (2019) [Google Scholar]
- T. Zabiński, T. Maoczka, J. Kluska, M. Madera, and J. Sȩp, in Procedia CIRP 79, 63-67, (2019) [CrossRef] [Google Scholar]
- Y. C. Liang, S. Wang, W. D. Li, and X. Lu, Engineering, 5(4), 646–652, (2019) [CrossRef] [Google Scholar]
- Extreme Rare Event Classification Using Autoencoders In Keras: Preparing For GRE [last accessed on October 1, 2019] [Google Scholar]
- Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. [Google Scholar]
- M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, IEEE Commun. Surv. Tutorials 20, 2923--2960. (2018) [CrossRef] [Google Scholar]
- A. Agogino and K. Goebel (2007). BEST lab, UC Berkeley. “Milling Data Set”, NASA Ames Prognostics Data Repository [Google Scholar]
- A. Saxena and K. Goebel (2008). “Turbofan Engine Degradation Simulation Data Set”, NASA Ames Prognostics Data Repository. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.