Adaptive Canonical Variate Analysis for Machine Fault Detection and Identification
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-48021-9_86
Reference21 articles.
1. Cárcel, C.R., Cao, Y., Mba, D.: A benchmark of canonical variate analysis for fault detection and diagnosis. In: 2014 UKACC International Conference on Control, 9th–11th July 2014, Loughborough, U.K., pp. 329–356 (2007). https://doi.org/10.1016/S0167-4137(07)80012-6
2. Guerra, C.J., Kolodziej, J.R.: A data-driven approach for condition monitoring of reciprocating compressor valves. J. Eng. Gas Turb. Power 136(1), 1–13 (2017). https://doi.org/10.1115/1.4025944
3. Harrou, F., Nounou, M.N., Nounou, H.N., Madakyaru, M.: Statistical fault detection using PCA-based GLR hypothesis testing. J. Loss Prev. Process Ind. 26 (2013). https://doi.org/10.1016/j.jlp.2012.10.003
4. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Hoboken (2004)
5. Jiang, B., Huang, D., Zhu, X., Yang, F., Braatz, R.D.: Canonical variate analysis-based contributions for fault identification. J. Process Control 26, 17–25 (2015). https://doi.org/10.1016/j.jprocont.2014.12.001
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