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| Fault Diagnosis of Papermaking Wastewater Treatment Processes Based on Support Vector Machine |
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| DOI：10.11981/j.issn.1000-6842.2018.03.55 |
| Key Words:fault detection; fault diagnosis; principal component analysis; Mahalanobis distance discriminant analysis; support vector machine |
| Fund Project:制浆造纸工程国家重点实验室开放基金资助项目（201813,201610）；南京林业大学高层次人才科研启动基金（163105996）；江苏省制浆造纸科学与技术重点实验室开放基金项目（201530）。 |
| Author Name | Affiliation | | LI Xiang-yu1 | 1. Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | YANG-Chong1 | 1. Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | SONG-Liu1 | 1. Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | ZHAO Xiao yan1 | 1. Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | LIU Hong-bin1,2,3,* | 1. Jiangsu Provincial Key Lab of Pulp and Paper Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037
2.State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640； 3.Co Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 |
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| Abstract: |
| Fault detection and fault diagnosis are main topics in the industrial process monitoring field. Concerning the complicated character-ristics of papermaking wastewater treatment processes (WWTP), principal component analysis (PCA) was used for the fault detection, firsthy. Then Mahalanobis distance discriminant analysis and support vector machine (SVM) were used for the fault diagnosis of three constructed types of sensor faults, respectively. The results showed that the fault detection rate using PCA was 97.50%. The separation rates using SVM and Mahalanobis distance discriminant analysis were 88.75% and 76.25%, respectively. Compared with the fault diagnosis method based on Mahalanobis distance discriminant analysis, the fault diagnosis method based on SVM was more suitable for the papermaking WWTP. |
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