The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances.Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturba...The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances.Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturbance modeled by an exo-system and norm bounded uncertain disturbance.A nonlinear disturbance observer is designed to estimate the modeled disturbance.Then,the fault diagnosis observer is constructed by integrating disturbance observer with disturbance attenuation and rejection performances.The augmented Lyapunov functional approach,which involves the tuning parameter and slack variable,is applied to make the solution of inequality more flexible.Finally,applications for a two-link robotic manipulator system are given to show the efficiency of the proposed approach.展开更多
混杂系统包含有离散子系统和连续子系统,系统中变量转换复杂,参数存在不确定性,导致故障诊断的误报率较高。针对此问题,以单相全桥逆变器为研究对象,提出运用线性分式变换的键合图(Bond Graph in Linear Fractional Transformation,BG-L...混杂系统包含有离散子系统和连续子系统,系统中变量转换复杂,参数存在不确定性,导致故障诊断的误报率较高。针对此问题,以单相全桥逆变器为研究对象,提出运用线性分式变换的键合图(Bond Graph in Linear Fractional Transformation,BG-LFT),建立系统参数不确定性混合诊断键合图(Diagnostic Hybrid Bond Graph,DHBG)模型,并根据模型产生自适应阈值。基于混杂键合图的因果关系和结构特性,从DHBG中导出所有有效模式下的鲁棒解析冗余关系,结合自适应阈值评价残差,实现混杂系统的鲁棒故障诊断。在20-sim中进行建模仿真,仿真结果验证了该方法的有效性。展开更多
The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the ...The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.展开更多
基金supported by the National Natural Science Foundation of China(6077401360925012)+1 种基金the National High Technology Research and Development Program of China(863 Program) (2008AA12A216)the National Basic Research Program of China (973 Program)(2009CB 724002)
文摘The fault diagnosis problem is investigated for a class of nonlinear neutral systems with multiple disturbances.Time-varying faults are considered and multiple disturbances are supposed to include the unknown disturbance modeled by an exo-system and norm bounded uncertain disturbance.A nonlinear disturbance observer is designed to estimate the modeled disturbance.Then,the fault diagnosis observer is constructed by integrating disturbance observer with disturbance attenuation and rejection performances.The augmented Lyapunov functional approach,which involves the tuning parameter and slack variable,is applied to make the solution of inequality more flexible.Finally,applications for a two-link robotic manipulator system are given to show the efficiency of the proposed approach.
文摘混杂系统包含有离散子系统和连续子系统,系统中变量转换复杂,参数存在不确定性,导致故障诊断的误报率较高。针对此问题,以单相全桥逆变器为研究对象,提出运用线性分式变换的键合图(Bond Graph in Linear Fractional Transformation,BG-LFT),建立系统参数不确定性混合诊断键合图(Diagnostic Hybrid Bond Graph,DHBG)模型,并根据模型产生自适应阈值。基于混杂键合图的因果关系和结构特性,从DHBG中导出所有有效模式下的鲁棒解析冗余关系,结合自适应阈值评价残差,实现混杂系统的鲁棒故障诊断。在20-sim中进行建模仿真,仿真结果验证了该方法的有效性。
基金supported by the National Natural Science Foundation of China (61202078 61071139)the National High Technology Research and Development Program of China (863 Program)(SQ2011AA110101)
文摘The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration （RCA）, which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.
