An approach to robust fault diagnosis in mechanical systems using computational intelligence
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
http://link.springer.com/content/pdf/10.1007/s10845-017-1343-1.pdf
Reference63 articles.
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3. Bedoya, C., Uribe, C., & Isaza, C. (2012). Unsupervised feature selection based on fuzzy clustering for fault detection of the Tennessee Eastman process. Advances in artificial intelligence (LNAI 7637 pp. 350 – 360), Springer-Verlag.
4. Bernal de Lázaro, J. M., Llanes-Santiago, O., Prieto Moreno, A., Knupp, D. C., & Silva-Neto, A. J. (2016). Enhanced dynamic approach to improve the detection of small-magnitude faults. Chemical Engineering Science, 146, 166–179.
5. Bernal de Lázaro, J. M., Prieto Moreno, A., Llanes-Santiago, O., & Silva Neto, A. J. (2015). Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems. Computers & Industrial Engineering, 87, 140–149.
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