Fault diagnosis of output‐related processes with multi‐block MOPLS

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EarlyView Article

  • Published: Jul 28, 2017
  • Author: Rongrong Sun, Yingwei Zhang, Lin Feng, Xuguang Li

For fault diagnosis of output‐related processes, a relatively high false alarm rate (FAR) of output‐irrelevant faults exists because the output‐irrelevant variables are not removed completely by conventional approaches. A relatively large number of computational loads is thus required. Therefore, in this paper, a new fault diagnosis approach based on multiblock modified orthogonal projections to latent structures is proposed to complete fault diagnosis for complex chemical processes, particularly for the penicillin fermentation process. The main contributions are as follows: (1) Multiblock orthogonal projections to latent structures are applied to remove the noncorrelated variables of input blocks, which requires a relatively low computational load. In addition, block scores are obtained by block weights rather than super weights to better describe the character of each subblock. (2) Complete orthogonal decomposition between input block variables and output is explored to completely separate output‐related and output‐irrelevant variables to improve the accuracy of diagnosis. (3) A hierarchical diagnosis scheme, which is composed of block monitoring statistics and subblock monitoring statistics, is proposed to monitor and localize faults. Penicillin fermentation process is considered in this study, and the penicillin concentration‐relevant fault in each block and subblock is analyzed. The results of this study show that the proposed method has steadier performance on output‐related fault diagnoses and diagnoses faults more accurately.

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