At present, the method to study spatial variation of ground motions is statistic analysis based on dense array re-cords such as SMART-1 array, etc. For lacking of information of ground motions, there is no coherency f...At present, the method to study spatial variation of ground motions is statistic analysis based on dense array re-cords such as SMART-1 array, etc. For lacking of information of ground motions, there is no coherency function model of base rock and different style site. Spatial variation of ground motions in elastic media is analyzed by deterministic method in this paper. Taking elastic half-space model with dislocation source of fault, near-field ground motions are simulated. This model takes strike-slip fault and earth media into account. A coherency func-tion is proposed for base rock site.展开更多
水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。...水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。该方法首先采集了水电站监控系统中的设备状态数据、日志数据和配置数据,构建了包含输入层、隐含层和输出层的RBFNN故障诊断模型。其中输入层设置3个节点对应三类监控数据,输出层设置4个节点分别对应电气、机械、环境及其他监控部分的故障分类。通过参数寻优确定隐含层结构,并采用聚类方法确定基函数中心,使用最小二乘法优化网络权重,完成模型训练。实验依托某实际水电站监控系统展开,设计涵盖紧急至信息级的七级故障分类体系,并部署分层告警框架进行验证。结果表明,所提方法能有效抑制环境干扰引致的虚警,精准识别并分级呈现故障,仅在非关键性预警与信息级故障中各出现一次误判,显著优于对照方法。研究表明,所提方法能显著提升复杂工业背景下的故障辨识鲁棒性与告警直观性,为水电站监控系统的智能化运维提供了一种有效解决方案。展开更多
针对输油气管道的故障种类多、现场数据无法长期有效保存等问题,提出了一种基于边缘计算和改进随机向量函数链接(random vector functional-link,RVFL)网络的输油气管道故障分类方法。该方法扩展了监控和数据采集(supervisory control a...针对输油气管道的故障种类多、现场数据无法长期有效保存等问题,提出了一种基于边缘计算和改进随机向量函数链接(random vector functional-link,RVFL)网络的输油气管道故障分类方法。该方法扩展了监控和数据采集(supervisory control and data acquisition,SCADA)系统的功能,使其可以存储和访问大量的数据。首先,当输油气管道出现故障时,利用基于模糊似然函数的模糊聚类算法对故障发生前一段时间内的管道压力值进行聚类;然后,提取管道压力值密度特征,将其作为RVFL网络的增强节点,利用改进RVFL网络对故障进行分类。将改进RVFL网络部署在边缘计算模块中,对6种故障进行分类,其准确率可达到96.7%。展开更多
The accurate DC system model is the key to fault analysis and harmonic calculation of AC/DC system. In this paper, a frequency domain analysis model of DC system is established, and based on it a unified fundamental f...The accurate DC system model is the key to fault analysis and harmonic calculation of AC/DC system. In this paper, a frequency domain analysis model of DC system is established, and based on it a unified fundamental frequency and harmonic iterative calculation method is proposed. The DC system model is derived considering the dynamic switching characteristic of converter and the steady-state response features of dc control system synchronously. And the proposed harmonic calculation method fully considers the AC/DC harmonic interaction and fault interaction under AC asymmetric fault condition. The method is used to the harmonic analysis and calculation of CIGRE HVDC system. Compared with those obtained by simulation using PSCAD/EMTDC software, the results show that the proposed model and method are accurate and effective, and provides the analysis basis of harmonic suppression, filter configuration and protection analysis in AC/DC system.展开更多
The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimat...The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimation of general frequency response functions. Based on the fully decoupled Volterra identification algorithm, a new online fault diagnosis method based on nonlinear spectral analysis is presented, which can availably reduce the online compution requirements of general frequency response functions. The composition and working principle of the method are described, the test experiments have been done for damping spring of a vehicle suspension system by utilizing the new method, and the results indicate that the method is efficient.展开更多
A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measure...A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.展开更多
The additive fault tolerant control (FTC) for delayed system is studied in this work. To design the additive control, two steps are necessary;the first one is the estimation of the sensor fault amplitude using a Luenb...The additive fault tolerant control (FTC) for delayed system is studied in this work. To design the additive control, two steps are necessary;the first one is the estimation of the sensor fault amplitude using a Luenberger observer with delay, and the second one consists to generate the additive fault tolerant control law and to add it to the nominal control of delayed system. The additive control law must be in function of fault term, then, in the absence of fault the expression of additive control equal to zero. The generation of nominal control law consist to determinate the state feedback gain by using the Lambert W method. Around all these control tools, we propose an extension of the additive FTC to delayed singularly perturbed systems (SPS). So, this extension consists to decompose the delayed SPS in two parts: delayed slow subsystem (delayed SS) and fast subsystem (FS) without time delay. Next, we consider that the delayed SPS is affected at its steady-state, and we apply the principal of FTC to the delayed SS and finally we combine them with the feedback gain control of FS by using the principal of composite control.展开更多
Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the...Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.展开更多
基金National Natural Science Foundation of China (59895410)
文摘At present, the method to study spatial variation of ground motions is statistic analysis based on dense array re-cords such as SMART-1 array, etc. For lacking of information of ground motions, there is no coherency function model of base rock and different style site. Spatial variation of ground motions in elastic media is analyzed by deterministic method in this paper. Taking elastic half-space model with dislocation source of fault, near-field ground motions are simulated. This model takes strike-slip fault and earth media into account. A coherency func-tion is proposed for base rock site.
文摘水电站计算机监控系统长期运行于强电磁干扰、多源异构数据并发及工况瞬变的复杂工业环境中,导致传统监控方法虚警频发。为此,提出基于径向基函数神经网络(radial basis function neural network,RBFNN)的水电站监控系统智能告警方法。该方法首先采集了水电站监控系统中的设备状态数据、日志数据和配置数据,构建了包含输入层、隐含层和输出层的RBFNN故障诊断模型。其中输入层设置3个节点对应三类监控数据,输出层设置4个节点分别对应电气、机械、环境及其他监控部分的故障分类。通过参数寻优确定隐含层结构,并采用聚类方法确定基函数中心,使用最小二乘法优化网络权重,完成模型训练。实验依托某实际水电站监控系统展开,设计涵盖紧急至信息级的七级故障分类体系,并部署分层告警框架进行验证。结果表明,所提方法能有效抑制环境干扰引致的虚警,精准识别并分级呈现故障,仅在非关键性预警与信息级故障中各出现一次误判,显著优于对照方法。研究表明,所提方法能显著提升复杂工业背景下的故障辨识鲁棒性与告警直观性,为水电站监控系统的智能化运维提供了一种有效解决方案。
文摘针对输油气管道的故障种类多、现场数据无法长期有效保存等问题,提出了一种基于边缘计算和改进随机向量函数链接(random vector functional-link,RVFL)网络的输油气管道故障分类方法。该方法扩展了监控和数据采集(supervisory control and data acquisition,SCADA)系统的功能,使其可以存储和访问大量的数据。首先,当输油气管道出现故障时,利用基于模糊似然函数的模糊聚类算法对故障发生前一段时间内的管道压力值进行聚类;然后,提取管道压力值密度特征,将其作为RVFL网络的增强节点,利用改进RVFL网络对故障进行分类。将改进RVFL网络部署在边缘计算模块中,对6种故障进行分类,其准确率可达到96.7%。
文摘The accurate DC system model is the key to fault analysis and harmonic calculation of AC/DC system. In this paper, a frequency domain analysis model of DC system is established, and based on it a unified fundamental frequency and harmonic iterative calculation method is proposed. The DC system model is derived considering the dynamic switching characteristic of converter and the steady-state response features of dc control system synchronously. And the proposed harmonic calculation method fully considers the AC/DC harmonic interaction and fault interaction under AC asymmetric fault condition. The method is used to the harmonic analysis and calculation of CIGRE HVDC system. Compared with those obtained by simulation using PSCAD/EMTDC software, the results show that the proposed model and method are accurate and effective, and provides the analysis basis of harmonic suppression, filter configuration and protection analysis in AC/DC system.
文摘The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimation of general frequency response functions. Based on the fully decoupled Volterra identification algorithm, a new online fault diagnosis method based on nonlinear spectral analysis is presented, which can availably reduce the online compution requirements of general frequency response functions. The composition and working principle of the method are described, the test experiments have been done for damping spring of a vehicle suspension system by utilizing the new method, and the results indicate that the method is efficient.
基金supported by the UK Leverhulme Trust (F/00 120/BC)the National Natural Science Foundation of China (6082800760974029)
文摘A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.
文摘The additive fault tolerant control (FTC) for delayed system is studied in this work. To design the additive control, two steps are necessary;the first one is the estimation of the sensor fault amplitude using a Luenberger observer with delay, and the second one consists to generate the additive fault tolerant control law and to add it to the nominal control of delayed system. The additive control law must be in function of fault term, then, in the absence of fault the expression of additive control equal to zero. The generation of nominal control law consist to determinate the state feedback gain by using the Lambert W method. Around all these control tools, we propose an extension of the additive FTC to delayed singularly perturbed systems (SPS). So, this extension consists to decompose the delayed SPS in two parts: delayed slow subsystem (delayed SS) and fast subsystem (FS) without time delay. Next, we consider that the delayed SPS is affected at its steady-state, and we apply the principal of FTC to the delayed SS and finally we combine them with the feedback gain control of FS by using the principal of composite control.
文摘Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.
