Open Access
2014 A Partial Robust M-Regression-Based Prediction and Fault Detection Method
Jianfang Jiao, Jingxin Zhang, Hamid Reza Karimi
Abstr. Appl. Anal. 2014: 1-7 (2014). DOI: 10.1155/2014/304754


Due to its simplicity and easy implementation, partial least squares (PLS) serves as an efficient approach in large-scale industrial process. However, like many data-based methods, PLS is quite sensitive to outliers, which is a common abnormal characteristic of the measured process data that can significantly affect the monitoring performance of PLS. In order to develop a robust prediction and fault detection method, this paper employs the partial robust M-regression (PRM) to deal with the outliers. Moreover, to eliminate the useless variations for prediction, an orthogonal decomposition is performed on the measurable variables space so as to allow the new method to serve as a powerful tool for quality-related prediction and fault detection. The proposed method is finally applied on the Tennessee Eastman (TE) process.


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Jianfang Jiao. Jingxin Zhang. Hamid Reza Karimi. "A Partial Robust M-Regression-Based Prediction and Fault Detection Method." Abstr. Appl. Anal. 2014 1 - 7, 2014.


Published: 2014
First available in Project Euclid: 6 October 2014

zbMATH: 07022126
Digital Object Identifier: 10.1155/2014/304754

Rights: Copyright © 2014 Hindawi

Vol.2014 • 2014
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