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Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods
Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, an...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413088/ https://www.ncbi.nlm.nih.gov/pubmed/30813310 http://dx.doi.org/10.3390/s19040929 |