Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. Ho...Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.展开更多
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
Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control sy...Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.展开更多
针对传统阈值报警、人员巡检和人工检查参数来开展核电站状态监测的不足,以CANDU6反应堆主热传输泵(主泵)为例,开展了状态监测技术的研究,提出了主泵智能预警模型。采用分布式策略按照主泵部件功能及测点参数特征分别建立相应的监测模...针对传统阈值报警、人员巡检和人工检查参数来开展核电站状态监测的不足,以CANDU6反应堆主热传输泵(主泵)为例,开展了状态监测技术的研究,提出了主泵智能预警模型。采用分布式策略按照主泵部件功能及测点参数特征分别建立相应的监测模型。采用核主成分分析(Kernel Principal Component Analysis,KPCA)对各部件进行异常检测,在检测出异常后,采用贡献图法对故障进行定位,及时地识别出异常的参数。通过秦山3期CANDU6反应堆主泵的真实异常数据对模型和算法进行测试,测试结果表明该方法可以及时发现异常,并准确定位异常参数。展开更多
With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM...With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM architecture, TCG hardware and application-oriented ＂thin＂ virtual machine （VM）, Trusted VMM-based security architecture is present in this paper with the character of reduced and distributed trusted computing base （TCB）. It provides isolation and integrity guarantees based on which general security requirements can be satisfied.展开更多
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province of China(ZR2011FM014)+1 种基金the Doctoral Fund of Shandong Province(BS2012ZZ011)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.
基金National Natural Science Foundation of China (No. 61074079)Shanghai Leading Academic Discipline Project,China (No.B504)
文摘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.
基金Supported by the National Natural Science Foundation of China(61134007,61203157)the National Science Fund for Outstanding Young Scholars(61222303)+1 种基金the Fundamental Research Funds for the Central Universities(22A20151405)Shanghai R&D Platform Construction Program(13DZ2295300)
文摘Control performance monitoring has attracted great attention in both academia and industry over the past two decades. However, most research efforts have been devoted to the performance monitoring of linear control systems, without considering the pervasive nonlinearities(e.g. valve stiction) present in most industrial control systems. In this work, a novel probability distribution distance based index is proposed to monitor the performance of non-linear control systems. The proposed method uses Hellinger distance to evaluate change of control system performance. Several simulation examples are given to illustrate the effectiveness of the proposed method.
文摘针对传统阈值报警、人员巡检和人工检查参数来开展核电站状态监测的不足,以CANDU6反应堆主热传输泵(主泵)为例,开展了状态监测技术的研究,提出了主泵智能预警模型。采用分布式策略按照主泵部件功能及测点参数特征分别建立相应的监测模型。采用核主成分分析(Kernel Principal Component Analysis,KPCA)对各部件进行异常检测,在检测出异常后,采用贡献图法对故障进行定位,及时地识别出异常的参数。通过秦山3期CANDU6反应堆主泵的真实异常数据对模型和算法进行测试,测试结果表明该方法可以及时发现异常,并准确定位异常参数。
基金Supported by the National Program on Key Basic Re-search Project of China (G1999035801)
文摘With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM architecture, TCG hardware and application-oriented ＂thin＂ virtual machine （VM）, Trusted VMM-based security architecture is present in this paper with the character of reduced and distributed trusted computing base （TCB）. It provides isolation and integrity guarantees based on which general security requirements can be satisfied.
