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Distributed process monitoring based on Kantorovich distancemultiblock variational autoencoder and Bayesian inference
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作者 Zongyu Yao Qingchao Jiang Xingsheng Gu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第9期311-323,共13页
Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring sche... Modern industrial processes are typically characterized by large-scale and intricate internal relationships.Therefore,the distributed modeling process monitoring method is effective.A novel distributed monitoring scheme utilizing the Kantorovich distance-multiblock variational autoencoder(KD-MBVAE)is introduced.Firstly,given the high consistency of relevant variables within each sub-block during the change process,the variables exhibiting analogous statistical features are grouped into identical segments according to the optimal quality transfer theory.Subsequently,the variational autoencoder(VAE)model was separately established,and corresponding T^(2)statistics were calculated.To improve fault sensitivity further,a novel statistic,derived from Kantorovich distance,is introduced by analyzing model residuals from the perspective of probability distribution.The thresholds of both statistics were determined by kernel density estimation.Finally,monitoring results for both types of statistics within all blocks are amalgamated using Bayesian inference.Additionally,a novel approach for fault diagnosis is introduced.The feasibility and efficiency of the introduced scheme are verified through two cases. 展开更多
关键词 Chemical processes SAFETY Kantorovich distance Neural networks process monitoring Bayesian inference
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A process monitoring method for autoregressive-dynamic inner total latent structure projection
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作者 CHEN Yalin KONG Xiangyu LUO Jiayu 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第5期1326-1336,共11页
As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos... As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system. 展开更多
关键词 dynamic characteristic fault detection feature extraction process monitoring projection to latent structure(PLS) quality-related spatial partitioning
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Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA 被引量:5
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作者 张曦 阎威武 +1 位作者 赵旭 邵惠鹤 《Journal of Southeast University(English Edition)》 EI CAS 2007年第1期51-56,共6页
A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis (KPCA) and Fisher discriminant analysis (FDA) is presented. The basic idea of this method is:... A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis (KPCA) and Fisher discriminant analysis (FDA) is presented. The basic idea of this method is: First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator's fault data set prove that the proposed method is effective. 展开更多
关键词 NONLINEAR kernel PCA FDA process monitoring fault diagnosis CONDENSER
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Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry 被引量:39
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作者 梁军 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2003年第2期191-203,共13页
Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares ... Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made. 展开更多
关键词 multivariate statistical process monitoring and control (MSPM&C) fault detection and isolation (FDI) principal component analysis (PCA) partial least squares (PLS) quality control inferential model
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Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation 被引量:16
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作者 熊丽 梁军 钱积新 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2007年第4期524-532,共9页
Abstract Data-driven tools, such as principal component analysis (PCA) and independent component analysis (ICA) have been applied to different benchmarks as process monitoring methods. The difference between the t... Abstract Data-driven tools, such as principal component analysis (PCA) and independent component analysis (ICA) have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation (KDE) is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE(KPCA), and ICA with KDE,(KICA), are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator. 展开更多
关键词 multivariate statistical process monitoring principal comPonent analysis kermel density estimation POLYPROPYLENE catalyzer reactor fault detection data-driven tools
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On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis 被引量:14
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作者 郭辉 李宏光 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2013年第3期263-270,共8页
In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troubleso... In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e'io'evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods. 展开更多
关键词 batch process monitoring multi-way independent componerxt analysis system deviation Box-Coxtransformation
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Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework 被引量:5
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作者 Muhammad Nawaz Abdulhalim Shah Maulud +2 位作者 Haslinda Zabiri Syed Ali Ammar Taqvi Alamin Idris 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2021年第1期253-265,共13页
Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemi... Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety.Small or incipient irregularities may lead to severe degradation in complex chemical processes,and the conventional process monitoring techniques cannot detect these irregularities.In this study to improve the performance of monitoring,an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis(MSPCA) with cumulative sum(CUSUM) and exponentially weighted moving average(EWMA) control charts.The new Hotelling's T~2 and square prediction error(SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data.The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods.The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults.Moreover,a comparative study shows that the SPEEWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%. 展开更多
关键词 Chemical process system CSTR Fault detection Multiscale Principal component analysis process monitoring
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Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace 被引量:5
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作者 解翔 侍洪波 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1174-1179,共6页
For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring st... For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process. 展开更多
关键词 multimode process monitoring fuzzy C-means locality preserving projection integrated monitoring index Tennessee Eastman process
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A Novel MDFA-MKECA Method With Application to Industrial Batch Process Monitoring 被引量:4
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作者 Yinghua Yang Xiang Shi +1 位作者 Xiaozhi Liu Hongru Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1446-1454,共9页
For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy compon... For the complex batch process with characteristics of unequal batch data length,a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis(MDFA-MKECA)in this paper.Combining the mechanistic knowledge,different mixed data features of each batch including statistical and thermodynamics entropy features,are extracted to finish data pre-processing.After that,MKECA is applied to reduce data dimensionality and finally establish a monitoring model.The proposed method is applied to a reheating furnace industry process,and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process. 展开更多
关键词 MDFA MKECA process monitoring reheating furnace statistical features thermodynamics entropy feature
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Local component based principal component analysis model for multimode process monitoring 被引量:5
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作者 Yuan Li Dongsheng Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2021年第6期116-124,共9页
For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component b... For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate. 展开更多
关键词 Principal component analysis Finite Gaussian mixture model process monitoring Tennessee Eastman(TE)process
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Multimodal process monitoring based on transition-constrained Gaussian mixture model 被引量:4
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作者 Shutian Chen Qingchao Jiang Xuefeng Yan 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第12期3070-3078,共9页
Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challengi... Reliable process monitoring is important for ensuring process safety and product quality.A production process is generally characterized bymultiple operation modes,and monitoring thesemultimodal processes is challenging.Most multimodal monitoring methods rely on the assumption that the modes are independent of each other,which may not be appropriate for practical application.This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring.This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data.This process enables the identified modes to reflect the stability of actual working conditions,improve mode identification accuracy,and enhance monitoring reliability in cases of mode overlap.Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach inmultimodal process monitoring with mode overlap. 展开更多
关键词 Multimodal process monitoring Gaussian mixture model State transition matrix process control process systems Systems engineering
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A Robust Statistical Batch Process Monitoring Framework and Its Application 被引量:4
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作者 谢磊 张建明 王树青 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2004年第5期682-687,共6页
In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to laten... In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed. 展开更多
关键词 robust statistical batch process monitoring robust principal componentanalysis streptomycin fermentation robust multi-way principal component analysis
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On-Line Batch Process Monitoring Using Multiway Kernel Partial Least Squares 被引量:4
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作者 胡益 马贺贺 侍洪波 《Journal of Donghua University(English Edition)》 EI CAS 2011年第6期585-590,共6页
An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partia... An approach for batch processes monitoring and fault detection based on multiway kernel partial least squares(MKPLS) was presented.It is known that conventional batch process monitoring methods,such as multiway partial least squares(MPLS),are not suitable due to their intrinsic linearity when the variations are nonlinear.To address this issue,kernel partial least squares(KPLS) was used to capture the nonlinear relationship between the latent structures and predictive variables.In addition,KPLS requires only linear algebra and does not involve any nonlinear optimization.In this paper,the application of KPLS was extended to on-line monitoring of batch processes.The proposed batch monitoring method was applied to a simulation benchmark of fed-batch penicillin fermentation process.And the results demonstrate the superior monitoring performance of MKPLS in comparison to MPLS monitoring. 展开更多
关键词 process monitoring fault detection kernel partial least squares(KPLS) nonlinear process multiway kernel partial least squares(MKPLS)
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Batch process monitoring based on multilevel ICA-PCA 被引量:3
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作者 Zhi-qiang GE Zhi-huan SONG 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2008年第8期1061-1069,共9页
In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component a... In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method. 展开更多
关键词 MULTILEVEL Independent component analysis (ICA) Principal component analysis (PCA) Batch process monitoring NON-GAUSSIAN
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A Web-based machining process monitoring system for E-manufacturing implementation 被引量:2
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作者 SHIN Bong-cheol KIM Gun-hee +5 位作者 CHOI Jin-hwa JEON Byung-cheol LEE Honghee CHO Myeong-woo HAN Jin-yong PARK Dong-sam 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第9期1467-1473,共7页
Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries... Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments. 展开更多
关键词 E-MANUFACTURING Web-based system INTERNET Machining process monitoring Expert system
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A new process monitoring method based on noisy time structure independent component analysis 被引量:2
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作者 蔡连芳 田学民 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第1期162-172,共11页
Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers... Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method. 展开更多
关键词 process monitoring Independent component analysis Measurement noises KURTOSIS Mixing matrix Contribution plot Sensitivity analysis
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Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map 被引量:2
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作者 宋羽 姜庆超 颜学峰 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第2期601-609,共9页
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla... A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults. 展开更多
关键词 statistic pattern framework self-organizing map fault diagnosis process monitoring
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Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review 被引量:12
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作者 Lingbao Kong Xing Peng +2 位作者 Yao Chen Ping Wang Min Xu 《International Journal of Extreme Manufacturing》 2020年第2期1-27,共27页
Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities i... Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper. 展开更多
关键词 MULTI-SENSOR data fusion process monitoring additive manufacturing laser melting
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Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring 被引量:3
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作者 王丽 侍洪波 《Journal of Donghua University(English Edition)》 EI CAS 2009年第5期461-466,共6页
In this research, a new fault detection method based on kernel independent component analysis (kernel ICA) is developed. Kernel ICA is an improvement of independent component analysis (ICA), and is different from ... In this research, a new fault detection method based on kernel independent component analysis (kernel ICA) is developed. Kernel ICA is an improvement of independent component analysis (ICA), and is different from kernel principal component analysis (KPCA) proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 (the sum of the squared independent scores) and squared prediction error (SPE) charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring. 展开更多
关键词 process monitoring fault detection kernelindependent component analysis
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Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA 被引量:2
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作者 张曦 阎威武 +1 位作者 赵旭 邵惠鹤 《Journal of Donghua University(English Edition)》 EI CAS 2007年第5期587-593,共7页
A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) ... A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) plus ICA.KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel.ICA seeks the projection directions in the KPCA whitened space,making the distribution of the projected data as non-gaussian as possible.The application to the fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA. 展开更多
关键词 kernel ICA NONLINEAR fault detection process monitoring FCCU process
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