Anomaly detection for process monitoring based on machine learning technique

Author:

Hamrouni Imen,Lahdhiri Hajer,Ben Abdellafou Khaoula,Aljuhani Ahamed,Taouali OkbaORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference47 articles.

1. Bounoua W, et al (2019) Online monitoring scheme. Using principal component analysis through Kullback-Leibler. Divergence analysis. Technique for fault detection. Trans Inst Meas Control 57–101.

2. Russell EL, Chiang LH, Braatz RD (2012) Data-driven methods for fault detection and diagnosis in chemical processes. Springer, New York

3. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Phylosophical Mag J Sci 6:559–572

4. Hotelling H (1947) Techniques of statis-tical analysis- multivariate quality control-illustrated by air testing of sample bombsights. Mcgraw-Hill, New York, pp 11–148

5. Jolliffe IT (2002) Principal component analysis. Springer series in statistics. Springer, New York

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