Supervised process monitoring and fault diagnosis based on machine learning methods-Reference-Cited by-同舟云学术

Supervised process monitoring and fault diagnosis based on machine learning methods

Published:2019-02-01 Issue:5-8 Volume:102 Page:2321-2337
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Lahdhiri Hajer,Said Maroua,Abdellafou Khaoula Ben,Taouali Okba,Harkat Mohamed Faouzi

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/article/10.1007/s00170-019-03306-z/fulltext.html

Reference59 articles.

1. Jaffel I, Taouali O, Elaissi I, Messaoud H (2014) A new online fault detection method based on PCA technique. IMA J Math Control Inf 31(4):487–499

2. Tharrault Y, Mourot G, Ragot J, Maquin D (2008) Fault detection and isolation with robust principal component analysis. Int J Appl Math Comput Sci 18(4):429–442

3. Mika S, Schölkopf B, Smola AJ, Müller KR, Scholz M, Rätsch G (1998, December) Kernel PCA and de-noising in feature spaces. In: NIPS, vol 11, pp 536–542

4. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

5. Aronszajn N (1950) Theory of reproducing kernels. Trans Am Math Soc 68(3):337–404

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