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Jiao, Jianfang; Zhang, Jingxin; Karimi, Hamid Reza

A partial robust M-regression-based prediction and fault detection method. (English) Zbl 1449.62280

Abstr. Appl. Anal. 2014, Article ID 304754, 7 p. (2014).
Summary: 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.

MSC:

62P30 Applications of statistics in engineering and industry; control charts
62M20 Inference from stochastic processes and prediction
62G35 Nonparametric robustness
62G32 Statistics of extreme values; tail inference

Keywords:

M-regression-based prediction; outliers
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