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
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
The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.展开更多
In the present study,the effects of process parameters(output voltage x,nitrogen flux l and specific strengthening time s)on the microstructure and wear resistance properties of TiN coatings prepared by electrospark d...In the present study,the effects of process parameters(output voltage x,nitrogen flux l and specific strengthening time s)on the microstructure and wear resistance properties of TiN coatings prepared by electrospark deposition(ESD)were investigatedsystematically.The microstructure of the coatings was characterized for thickness(TOC),content of TiN(CON)and porosity(POC).A statistical model was developed to identify the significant factors affecting the microstructure and wear resistance of the coatings.The results show that the output voltage x and nitrogen flux l present significant effects on majority of the evaluation indexes such asTOC,friction coefficient(COF)and wear mass loss(Id),while the specific strengthening time s has a significant effect on POC and asmall effect on the other indexes.The optimal process parameters were obtained as follows:output voltage(x,60V),nitrogen flux(l,15L/min)and specific strengthening time(s,3min/cm2).The variation of wear mass loss(Id)by the variation of the outputvoltage(x)and nitrogen flux(l)is attributed to the change of wear mechanisms of TiN coatings.The main wear mechanism of TiNcoating prepared under optimal process parameters is micro-cutting wear accompanied by micro-fracture wear.展开更多
This paper addresses the problem of condition assessment of bridge expansion joints using long-term measurement data under changing environmental conditions.The effects of temperature,traffic loading and wind on the e...This paper addresses the problem of condition assessment of bridge expansion joints using long-term measurement data under changing environmental conditions.The effects of temperature,traffic loading and wind on the expansion joint displacements are analyzed and interpreted,which reveal that measured displacements are observed to increase with an increase in temperature and to decrease with increased traffic loading,while the correlation between displacement and wind speed is very weak.Two regression models are developed to simulate the varying displacements under the changes in temperature and traffic loadings.Based on these models,the effects of the environmental conditions are removed to obtain the normalized displacement.Statistical process control using mean value control charts is further used to detect damage to the bridge expansion joints.The results reveal that the proposed method had a good capability for detecting the damage-induced 1.0%variances of the annual changes in the expansion joint displacements.展开更多
基金Supported by the National High-Tech Development Program of China(No.863-511-920-011,2001AA411230).
文摘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.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘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.
基金Supported by the 973 project of China （2013CB733600）, the National Natural Science Foundation （21176073）, the Doctoral Fund of Ministry of Education （20090074110005）, the New Century Excellent Talents in University （NCET-09-0346）, ＂Shu Guang＂ project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
文摘In the present study,the effects of process parameters(output voltage x,nitrogen flux l and specific strengthening time s)on the microstructure and wear resistance properties of TiN coatings prepared by electrospark deposition(ESD)were investigatedsystematically.The microstructure of the coatings was characterized for thickness(TOC),content of TiN(CON)and porosity(POC).A statistical model was developed to identify the significant factors affecting the microstructure and wear resistance of the coatings.The results show that the output voltage x and nitrogen flux l present significant effects on majority of the evaluation indexes such asTOC,friction coefficient(COF)and wear mass loss(Id),while the specific strengthening time s has a significant effect on POC and asmall effect on the other indexes.The optimal process parameters were obtained as follows:output voltage(x,60V),nitrogen flux(l,15L/min)and specific strengthening time(s,3min/cm2).The variation of wear mass loss(Id)by the variation of the outputvoltage(x)and nitrogen flux(l)is attributed to the change of wear mechanisms of TiN coatings.The main wear mechanism of TiNcoating prepared under optimal process parameters is micro-cutting wear accompanied by micro-fracture wear.
文摘针对核独立元分析(kernel independent component analysis,KICA)在非线性动态过程中对微小故障检测率低的问题,提出一种基于加权统计特征KICA(weighted statistical feature KICA,WSFKICA)的故障检测与诊断方法。首先,利用KICA从原始数据中捕获独立元数据和残差数据;然后,通过加权统计特征和滑动窗口获取改进统计特征数据集,并由此数据集构建统计量进行故障检测;最后,利用基于变量贡献图的方法进行过程故障诊断。与传统KICA统计量相比,所提方法的统计量对非线性动态过程中的微小故障具有更高的故障检测性能。应用该方法对一个数值例子和田纳西-伊斯曼(Tennessee-Eastman,TE)过程进行仿真测试,仿真结果显示出所提方法相对于独立元分析(ICA)、KICA、核主成分分析(kernel principal component analysis,KPCA)和统计局部核主成分分析(statistical local kernel principal component analysis,SLKPCA)检测的优势。
基金the support of the National Natural Science Foundation of China(Grant Nos.50725828,50808041 and 50978056)the Ph.D.Program Foundation of the Ministry of Education of China(Grant No.200802861011)the Teaching and Research Foundation for Excellent Young Teachers of Southeast University.
文摘This paper addresses the problem of condition assessment of bridge expansion joints using long-term measurement data under changing environmental conditions.The effects of temperature,traffic loading and wind on the expansion joint displacements are analyzed and interpreted,which reveal that measured displacements are observed to increase with an increase in temperature and to decrease with increased traffic loading,while the correlation between displacement and wind speed is very weak.Two regression models are developed to simulate the varying displacements under the changes in temperature and traffic loadings.Based on these models,the effects of the environmental conditions are removed to obtain the normalized displacement.Statistical process control using mean value control charts is further used to detect damage to the bridge expansion joints.The results reveal that the proposed method had a good capability for detecting the damage-induced 1.0%variances of the annual changes in the expansion joint displacements.
