Fault Diagnosis for TE Process Using RBF Neural Network
Fault diagnosis of industrial process has long been a challenging issue owing to the industrial system that exhibits nonlinearity, coupled parameters and time-varying in the production process. This paper presents a novel dynamic fault diagnosis model (AUKF-RBF) based on radial basis function (RBF)...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9521500/ |
| _version_ | 1819134196338130944 |
|---|---|
| author | Xin Liu Hai He |
| author_facet | Xin Liu Hai He |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | Fault diagnosis of industrial process has long been a challenging issue owing to the industrial system that exhibits nonlinearity, coupled parameters and time-varying in the production process. This paper presents a novel dynamic fault diagnosis model (AUKF-RBF) based on radial basis function (RBF) neural network for Tennessee Eastman (TE) industrial process. In order to effectively reflect the dynamic features of industrial system, a dynamic fault diagnosis model is established based on UKF and RBF neural network. In particular, UKF is used to optimize the weights, the center, and the width of the hidden layer nodes of RBF. Furthermore, to reduce the effect of the inappropriate initial filter parameters in UKF, an adaptive factor <inline-formula> <tex-math notation="LaTeX">$\delta _{k}$ </tex-math></inline-formula> is developed to tune the covariance matrix adaptively. Finally, the proposed fault diagnosis algorithm is applied to TE benchmark industrial process. Experimental results show the effectiveness of the proposed fault diagnosis method. |
| first_indexed | 2024-12-22T09:59:21Z |
| format | Article |
| id | doaj.art-acb2f3c703894cf4b67b4206170caf65 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:59:21Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-acb2f3c703894cf4b67b4206170caf652022-12-21T18:30:10ZengIEEEIEEE Access2169-35362021-01-01911845311846010.1109/ACCESS.2021.31073609521500Fault Diagnosis for TE Process Using RBF Neural NetworkXin Liu0https://orcid.org/0000-0002-3084-7128Hai He1https://orcid.org/0000-0003-2580-2707Chongqing City Management College, Chongqing, ChinaChongqing City Management College, Chongqing, ChinaFault diagnosis of industrial process has long been a challenging issue owing to the industrial system that exhibits nonlinearity, coupled parameters and time-varying in the production process. This paper presents a novel dynamic fault diagnosis model (AUKF-RBF) based on radial basis function (RBF) neural network for Tennessee Eastman (TE) industrial process. In order to effectively reflect the dynamic features of industrial system, a dynamic fault diagnosis model is established based on UKF and RBF neural network. In particular, UKF is used to optimize the weights, the center, and the width of the hidden layer nodes of RBF. Furthermore, to reduce the effect of the inappropriate initial filter parameters in UKF, an adaptive factor <inline-formula> <tex-math notation="LaTeX">$\delta _{k}$ </tex-math></inline-formula> is developed to tune the covariance matrix adaptively. Finally, the proposed fault diagnosis algorithm is applied to TE benchmark industrial process. Experimental results show the effectiveness of the proposed fault diagnosis method.https://ieeexplore.ieee.org/document/9521500/Fault diagnosisradial basis functionunscented Kalman filterTennessee Eastman processdynamic modeling |
| spellingShingle | Xin Liu Hai He Fault Diagnosis for TE Process Using RBF Neural Network IEEE Access Fault diagnosis radial basis function unscented Kalman filter Tennessee Eastman process dynamic modeling |
| title | Fault Diagnosis for TE Process Using RBF Neural Network |
| title_full | Fault Diagnosis for TE Process Using RBF Neural Network |
| title_fullStr | Fault Diagnosis for TE Process Using RBF Neural Network |
| title_full_unstemmed | Fault Diagnosis for TE Process Using RBF Neural Network |
| title_short | Fault Diagnosis for TE Process Using RBF Neural Network |
| title_sort | fault diagnosis for te process using rbf neural network |
| topic | Fault diagnosis radial basis function unscented Kalman filter Tennessee Eastman process dynamic modeling |
| url | https://ieeexplore.ieee.org/document/9521500/ |
| work_keys_str_mv | AT xinliu faultdiagnosisforteprocessusingrbfneuralnetwork AT haihe faultdiagnosisforteprocessusingrbfneuralnetwork |