Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a nove...Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.展开更多
Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-...Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.展开更多
基金Project (61203021) supported by the National Natural Science Foundation of ChinaProject (2011216011) supported by the Key Science and Technology Program of Liaoning Province,China+1 种基金Project (2013020024) supported by the Natural Science Foundation of Liaoning Province,ChinaProject (LJQ2015061) supported by the Program for Liaoning Excellent Talents in Universities,China
文摘Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.
基金Supported by the National Natural Science Foundation of China(61374166)the Doctoral Fund of Ministry of Education of China(20120010110010)the Natural Science Fund of Ningbo(2012A610001)
文摘Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.
基金National Technology Research and Development Program (863,206649AA03Z322)National Natural Science Foundation of China (50772005)Beijing Science and Technology Thematic Program (Z08000303220808)
