Abstract
Statistical data-driven process monitoring is critical for efficient operations of industrial processes. However, deviations from normal regions in the process data may or may not lead to poor quality of products. This paper proposes a new combined index for detecting output-relevant faults, which affect the output data, and studies the output-relevant fault detectability based on total projection to latent structures (T-PLS). Given actual fault direction, fault-free data can be reconstructed and output-relevant part of fault magnitude can be estimated. Two new methods are derived to extract output-relevant fault subspace from faulty data. A simulation example and a case study on the Tennessee Eastman process are used to show the effectiveness of the proposed methods. © 2010 American Chemical Society.
Original language | English |
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Pages (from-to) | 9175-9183 |
Number of pages | 9 |
Journal | Industrial and Engineering Chemistry Research |
Volume | 49 |
Issue number | 19 |
Early online date | 26 Aug 2010 |
DOIs | |
Publication status | Published - 6 Oct 2010 |
Externally published | Yes |
Funding
This work was supported by the national 973 projects under Grants 2010CB731800 and 20090332602, by NSFC under Grants 60721003 and 60736026, and by the Changjiang Professorship (S.J.Q.) by the Ministry of Education, P.R. China.