Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal ...Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.展开更多
Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance de...Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.展开更多
In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new...In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.展开更多
Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tole...Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters＇ variation fault and load torque disturbance.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
The purpose of this work is the study of a mathematical model to discretize cracks at continuous mechanical systems, applying all the available properties at computational algorithm using the methodology of state obse...The purpose of this work is the study of a mathematical model to discretize cracks at continuous mechanical systems, applying all the available properties at computational algorithm using the methodology of state observers to detect, localize and evaluate the crack conditions, seeking the model limitations through an experiment developed at the mechanical department of UNESP, llha Solteira, S^o Paulo-Brazil. Three different notch sizes were placed, one by one, at the top surface of a cantilever beam （to be considered as a crack at the mechanical system） and harmonic forces were applied at the tip of the beam with three different frequencies, for each notch size, to obtain experimental data to run the diagnosis algorithm. From the results it was possible to infer that the observation system performance increases with the raising of the crack size, which can be explained by the model, that gets more accurate with bigger crack sizes, however, when the propagation of the crack is considered at the model, the diagnosis of the crack presence tends to be more difficult. It was also possible to conclude that the developed algorithm works properly for systems which excitation frequencies are higher than 20 Hz and different from the natural frequencies of the system, due to influence of dynamic response of the crack at the model.展开更多
基金Project(1390/2)supported by Khuzestan Gas Company,Iran
文摘Fault diagnostics is an important research area including different techniques.Principal component analysis(PCA)is a linear technique which has been widely used.For nonlinear processes,however,the nonlinear principal component analysis(NLPCA)should be applied.In this work,NLPCA based on auto-associative neural network(AANN)was applied to model a chemical process using historical data.First,the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN(E-AANN)was presented to isolate and reconstruct the faulty sensor simultaneously.The proposed method was implemented on a continuous stirred tank heater(CSTH)and used to detect and isolate two types of faults(drift and offset)for a sensor.The results show that the proposed method can detect,isolate and reconstruct the occurred fault properly.
基金Supported by the National Natural Science Foundation of China （61273160）, the Natural Science Foundation of Shandong Province of China （ZR2011FM014） and the Fundamental Research Funds for the Central Universities （10CX04046A）.
文摘Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.
基金Supported by the Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities(YYY11076)
文摘In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.
基金Major State Basic Research Development Program,China(No.2005CB221505)Special Scientific Research Foundation for Doctoral Subject of Colleges and Universities in China(No.20050248058)
文摘Due to its great potentisl value in theory and application, fault-tolerant control atrategies of nonlinear systems, especially combining with intelligent control methods, have been a focus in the academe. A fault-tolerant control method based on fuzzy neural networks was presented for nonlinear systems in this paper. The fault parameters were designed to detect the fault, adaptive updating method was introduced to estimate and track fault, and fuzzy neural networks were used to adjust the fault parameters and construct automated fault diagnosis. And the fault compeusation control force, which was given by fault estimation, was used to realize adaptive fault-tolerant control. This framework leaded to a simple structure, an accurate detection, and a high robusmess. The simulation results in induction motor show that it is still able to work well with high dynamic performance and control precision under the condition of motor parameters＇ variation fault and load torque disturbance.
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.
文摘The purpose of this work is the study of a mathematical model to discretize cracks at continuous mechanical systems, applying all the available properties at computational algorithm using the methodology of state observers to detect, localize and evaluate the crack conditions, seeking the model limitations through an experiment developed at the mechanical department of UNESP, llha Solteira, S^o Paulo-Brazil. Three different notch sizes were placed, one by one, at the top surface of a cantilever beam （to be considered as a crack at the mechanical system） and harmonic forces were applied at the tip of the beam with three different frequencies, for each notch size, to obtain experimental data to run the diagnosis algorithm. From the results it was possible to infer that the observation system performance increases with the raising of the crack size, which can be explained by the model, that gets more accurate with bigger crack sizes, however, when the propagation of the crack is considered at the model, the diagnosis of the crack presence tends to be more difficult. It was also possible to conclude that the developed algorithm works properly for systems which excitation frequencies are higher than 20 Hz and different from the natural frequencies of the system, due to influence of dynamic response of the crack at the model.
