Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory
Fault diagnosis and identification are important goals in ensuring the safe production of industrial processes. This article proposes a data reconstruction method based on Center Nearest Neighbor (CNN) theory for fault diagnosis and abnormal variable identification. Firstly, the <i>k</i>...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/12/2035 |
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| author | Guozhu Wang Ruizhe Zhou Fei Li Xiang Li Xinmin Zhang |
| author_facet | Guozhu Wang Ruizhe Zhou Fei Li Xiang Li Xinmin Zhang |
| author_sort | Guozhu Wang |
| collection | DOAJ |
| description | Fault diagnosis and identification are important goals in ensuring the safe production of industrial processes. This article proposes a data reconstruction method based on Center Nearest Neighbor (CNN) theory for fault diagnosis and abnormal variable identification. Firstly, the <i>k</i>-nearest neighbor (<i>k</i>-NN) method is used to monitor the process and determine whether there is a fault. Secondly, when there is a fault, a high-precision CNN reconstruction algorithm is used to reconstruct each variable and calculate the reconstructed control index. The variable that reduces the control index the most is replaced with the reconstructed variable in sequence, and the iteration is carried out until the control index is within the control range, and all abnormal variables are finally determined. The accuracy of the CNN reconstruction method was verified through a numerical example. Additionally, it was confirmed that the method is not only suitable for fault diagnosis of a single sensor but also can be used sensor faults that occur simultaneously or propagate due to variable correlation. Finally, the effectiveness and applicability of the proposed method were validated through the penicillin fermentation process. |
| format | Article |
| id | doaj-art-66f050dfd8104b66ae0f46b1ff9ff46e |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-66f050dfd8104b66ae0f46b1ff9ff46e2025-08-20T03:27:18ZengMDPI AGMathematics2227-73902025-06-011312203510.3390/math13122035Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction TheoryGuozhu Wang0Ruizhe Zhou1Fei Li2Xiang Li3Xinmin Zhang4School of Cable Engineering, Henan Institute of Technology, Xinxiang 453003, ChinaSchool of Cable Engineering, Henan Institute of Technology, Xinxiang 453003, ChinaSchool of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243032, ChinaCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Control Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaFault diagnosis and identification are important goals in ensuring the safe production of industrial processes. This article proposes a data reconstruction method based on Center Nearest Neighbor (CNN) theory for fault diagnosis and abnormal variable identification. Firstly, the <i>k</i>-nearest neighbor (<i>k</i>-NN) method is used to monitor the process and determine whether there is a fault. Secondly, when there is a fault, a high-precision CNN reconstruction algorithm is used to reconstruct each variable and calculate the reconstructed control index. The variable that reduces the control index the most is replaced with the reconstructed variable in sequence, and the iteration is carried out until the control index is within the control range, and all abnormal variables are finally determined. The accuracy of the CNN reconstruction method was verified through a numerical example. Additionally, it was confirmed that the method is not only suitable for fault diagnosis of a single sensor but also can be used sensor faults that occur simultaneously or propagate due to variable correlation. Finally, the effectiveness and applicability of the proposed method were validated through the penicillin fermentation process.https://www.mdpi.com/2227-7390/13/12/2035<i>k</i>-NNcenter nearest neighbordata reconstructionfault detectionfault diagnosis |
| spellingShingle | Guozhu Wang Ruizhe Zhou Fei Li Xiang Li Xinmin Zhang Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory Mathematics <i>k</i>-NN center nearest neighbor data reconstruction fault detection fault diagnosis |
| title | Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory |
| title_full | Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory |
| title_fullStr | Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory |
| title_full_unstemmed | Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory |
| title_short | Fault Diagnosis and Identification of Abnormal Variables Based on Center Nearest Neighbor Reconstruction Theory |
| title_sort | fault diagnosis and identification of abnormal variables based on center nearest neighbor reconstruction theory |
| topic | <i>k</i>-NN center nearest neighbor data reconstruction fault detection fault diagnosis |
| url | https://www.mdpi.com/2227-7390/13/12/2035 |
| work_keys_str_mv | AT guozhuwang faultdiagnosisandidentificationofabnormalvariablesbasedoncenternearestneighborreconstructiontheory AT ruizhezhou faultdiagnosisandidentificationofabnormalvariablesbasedoncenternearestneighborreconstructiontheory AT feili faultdiagnosisandidentificationofabnormalvariablesbasedoncenternearestneighborreconstructiontheory AT xiangli faultdiagnosisandidentificationofabnormalvariablesbasedoncenternearestneighborreconstructiontheory AT xinminzhang faultdiagnosisandidentificationofabnormalvariablesbasedoncenternearestneighborreconstructiontheory |