A Partial Robust M-Regression-Based Prediction and Fault Detection Method

Jianfang Jiao,Jingxin Zhang,Hamid Reza Karimi

doi:10.1155/2014/304754

Jianfang Jiao, Jingxin Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2014/304754

Copy DOI

Abstract

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.

Highlights

With the rapid development of modern science and technology, the industrial production processes become more automated and more complicated
The proposed scheme will be applied on the Tennessee Eastman (TE) benchmark
Two tasks are involved in the simulation, that is, quality-related prediction and fault detection

Summary

Introduction

With the rapid development of modern science and technology, the industrial production processes become more automated and more complicated. One drawback of PLS is that it is very sensitive to the abnormal characteristics of the measured process data, for example, outliers, which may be caused by various reasons like formatting errors, hardware failure, nonrepresentative sampling, and so forth. To overcome the drawback of classical PLS, many robust versions of PLS had been proposed [19,20,21] All these methods either suffer from nonrobust to high leverage points or are not efficient enough. To develop a robust and efficient method, Serneels et al [22] proposed a partial robust M-regression (PRM) approach which weakens the effect of outliers by choosing a proper weighting scheme with relative less computational load. PRM has become a popular method and a matlab toolbox had been developed

Objectives

Results

Conclusion