A Partial Robust M-Regression-Based Prediction and Fault Detection Method
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 sig...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/304754 |
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| _version_ | 1832563926166929408 |
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| author | Jianfang Jiao Jingxin Zhang Hamid Reza Karimi |
| author_facet | Jianfang Jiao Jingxin Zhang Hamid Reza Karimi |
| author_sort | Jianfang Jiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-823e7f4d49c547b4b2104a54e9b4e4f4 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-823e7f4d49c547b4b2104a54e9b4e4f42025-02-03T01:12:18ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/304754304754A Partial Robust M-Regression-Based Prediction and Fault Detection MethodJianfang Jiao0Jingxin Zhang1Hamid Reza Karimi2College of Engineering, Bohai University, Jinzhou 121013, ChinaCollege of Engineering, Bohai University, Jinzhou 121013, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayDue 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.http://dx.doi.org/10.1155/2014/304754 |
| spellingShingle | Jianfang Jiao Jingxin Zhang Hamid Reza Karimi A Partial Robust M-Regression-Based Prediction and Fault Detection Method Abstract and Applied Analysis |
| title | A Partial Robust M-Regression-Based Prediction and Fault Detection Method |
| title_full | A Partial Robust M-Regression-Based Prediction and Fault Detection Method |
| title_fullStr | A Partial Robust M-Regression-Based Prediction and Fault Detection Method |
| title_full_unstemmed | A Partial Robust M-Regression-Based Prediction and Fault Detection Method |
| title_short | A Partial Robust M-Regression-Based Prediction and Fault Detection Method |
| title_sort | partial robust m regression based prediction and fault detection method |
| url | http://dx.doi.org/10.1155/2014/304754 |
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