期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 135, 期 -, 页码 76-89出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2014.04.001
关键词
KPLS modeling; Fault detection; Fault diagnosis; Prediction risk assessment; Non-linear processes
类别
资金
- CONICET
- MinCyT
- Universidad Nacional del Litoral
- Universidad Tecnologica Nacional (Argentina)
The kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to non-linear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a non-linear process. The effectiveness of the proposed methods is confirmed by using simulation examples. (C) 2014 Elsevier B.V. All rights reserved.
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