Fault Detection and Identification Based on the Neighborhood Standardized Local Outlier Factor Method
| Autor: | Hehe Ma, Yi Hu, Hongbo Shi |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Normalization (statistics)
Local outlier factor Computer science General Chemical Engineering Gaussian Process (computing) General Chemistry computer.software_genre Industrial and Manufacturing Engineering Euclidean distance Identification (information) symbols.namesake Outlier symbols Data mining computer Statistic |
| Zdroj: | Industrial & Engineering Chemistry Research. 52:2389-2402 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/ie302042c |
| Popis: | Complex chemical processes often have multiple operating modes to meet changes in production conditions. At the same time, the within-mode process data usually follow a complex combination of Gaussian and non-Gaussian distributions. The multimodality and the within-mode distribution uncertainty in multimode operating data make conventional multivariate statistical process monitoring (MSPM) methods unsuitable for practical complex processes. In this work, a novel method called neighborhood standardized local outlier factor (NSLOF) method is proposed. The local outlier factor of each sample, which means the degree of being an outlier, is used as a monitoring statistic. A new normalized Euclidean distance based on the local neighborhood standardization strategy is employed during the calculation of the monitoring index. Then, a contribution-based fault identification method is developed. Instead of building multiple monitoring models for complex chemical processes with different operating conditions, the pro... |
| Databáze: | OpenAIRE |
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