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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xie Xiang Shi Hongbo |
| Copyright Year | 2012 |
| Description | Author affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai, 200237 (Xie Xiang; Shi Hongbo) |
| Abstract | An improved GMM (Gaussian mixture model) based batch process monitoring approach is proposed in this article to handle batch processes with multiple operating phases. GMM is an effective tool to construct monitoring models by estimating separate probability density functions of the nominal batch data. However, the existing GMM based monitoring method has the following disadvantages: (1) GMM utilize all the observed variables for online monitoring, which are computationally intensive for complex processes with dozens of variables. (2) Different measure units of variables will impact the monitoring results significantly since there is no auto-scaling procedure. (3) The trajectory of faulty is likely to fall within normal areas of other Gaussian components, which will leads to obvious false negatives. To overcome these deficiencies, an NPE (neighborhood preserving embedding) algorithm is introduced to generate an enhanced monitoring subspace, which not only facilitates the computational burden of training and utilizing GMMs, but also improves sensitivity to incipient fault symptoms. The efficiency of the proposed method is verified through a simulated fed-batch penicillin fermentation process. |
| File Size | 208543 |
| File Format | |
| ISBN | 9781467325813 |
| ISSN | 21612927 |
| e-ISBN | 9789881563811 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-25 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Chinese Assoc of Automati |
| Subject Keyword | Training Sensitivity neighborhood preserving embedding Batch production systems Trajectory Biomedical monitoring Batch process monitoring Monitoring Gaussian mixture model Principal component analysis penicillin fermentation |
| Content Type | Text |
| Resource Type | Article |
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