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Isolating incipient sensor fault based on recursive transformed component statistical analysis
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DOI：10.1016/j.jprocont.2018.01.002.png)
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This paper considers the isolation problem of incipient sensor fault. Based on recursive transformed component statistical analysis (RTCSA), two different isolation methods are proposed. The first method is called subspace reconstruction, where elements in specific subspaces are eliminated, and then reconstructed by minimizing the reconstructed detection index. The faulty variable is determined by the least scaled reconstructed detection index. The second method is called subblock detection, which has less online computational complexity. The subblocks of the measurement matrix are sequentially selected in each sliding window to calculate the subblock detection indices, and the faulty variable is determined by the largest subblock detection margin. Compared with the existing isolation methods such as reconstruction-based contribution (RBC) and its variant termed as average residual-difference reconstruction contribution plot (ARdR-CP), the superior isolation performances of the proposed methods are illustrated by a numerical example as well as a simulation on a continuous stirred tank reactor. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Fault isolation
Incipient sensor fault
Subspace reconstruction
Subblock detection
Recursive transformed component
statistical analysis
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