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Soft Fault Diagnosis of Analog Circuit Based on Particle Swarm Optimization
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作者 Long-Fu Zhou Yi-Bing Shi Wei Zhang 《Journal of Electronic Science and Technology of China》 2009年第4期358-361,共4页
A single soft fault diagnosis method for analog circuit with tolerance based on particle swarm optimization (PSO) is proposed. The parameter deviation of circuit elements is defined as the element of particle. Node-... A single soft fault diagnosis method for analog circuit with tolerance based on particle swarm optimization (PSO) is proposed. The parameter deviation of circuit elements is defined as the element of particle. Node-voltage incremental equations based on the sensitivity analysis are built as constraints of a linear programming (LP) equation. Through inducing the penalty coefficient, the LP equation is set as the fitness function for the PSO program. After evaluating the best position of particles, the position of the optimal particle states whether the actual parameter is within tolerance range or not. Simulation result shows the effectiveness of the method. 展开更多
关键词 Analog circuit DIAGNOSIS linear program particle swarm optimization soft fault.
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Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks 被引量:6
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作者 胡梅 王红 +1 位作者 胡庚 杨士元 《Tsinghua Science and Technology》 SCIE EI CAS 2007年第S1期26-31,共6页
Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault... Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising. 展开更多
关键词 soft fault diagnosis analog circuit back propagation neural network (BPNN) voltage relation function SLOPE
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An adaptive particle filter for soft fault compensation of mobile robots 被引量:8
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作者 DUAN ZhuoHua CAI ZiXing YU JinXia 《Science in China(Series F)》 2008年第12期2033-2046,共14页
Soft fault compensation plays an important role in mobile robot locating, mapping, and navigating. It is difficult to achieve fast and accurate compensation for mobile robots because they are usually highly non-linear... Soft fault compensation plays an important role in mobile robot locating, mapping, and navigating. It is difficult to achieve fast and accurate compensation for mobile robots because they are usually highly non-linear, non-Gaussian systems with limited computation and memory resources. An adaptive particle filter is presented to compensate two kinds of soft faults for mobile robots, i.e., noise or factor faults of dead reckoning sensors and slippage of wheels. Firstly, the kinematics models and the fault models are discussed, and five kinds of residual features are extracted to detect soft faults. Secondly, an adaptive particle filter is designed for fault compensation, and two kinds of adaptive scheme are discussed: 1) the noise variances of linear speed and yaw rate are adjusted according to residual features; 2) the particle number is adapted according to Kullback-Leibler divergence (KLD) of two approximate distribution denoted with two particle sets with different particles, i.e., increasing particle number if the KLD is large and decreasing particle number if the KLD is small. The theoretic proof is given and experimental results show the efficiency and accuracy of the presented approach. 展开更多
关键词 soft fault detection and compensation ADAPTIVE particle filter mobile robots
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Diagnosis of soft faults in analog integrated circuits based on fractional correlation 被引量:2
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作者 邓勇 师奕兵 张伟 《Journal of Semiconductors》 EI CAS CSCD 2012年第8期117-122,共6页
Aiming at the problem of diagnosing soft faults in analog integrated circuits, an approach based on fractional correlation is proposed. First, the Volterra series of the circuit under test (CUT) decomposed by the fr... Aiming at the problem of diagnosing soft faults in analog integrated circuits, an approach based on fractional correlation is proposed. First, the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions. Then, the calculated fractional correlation functions are used to form the fault signatures of the CUT. By comparing the fault signatures, the different soft faulty conditions of the CUT are identified and the faults are located. Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits. 展开更多
关键词 analog circuits soft faults fault diagnosis Volterra series fractional correlation
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Method for Analog Circuit Soft-Fault Diagnosis and Parameter Identification Based on Indictor of Phase Deviation and Spectral Radius
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作者 Qi-Zhong Zhou Yong-Le Xie 《Journal of Electronic Science and Technology》 CAS CSCD 2017年第3期313-323,共11页
The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnos... The soft fault induced by parameter variation is one of the most challenging problems in the domain of fault diagnosis for analog circuits.A new fault location and parameter prediction approach for soft-faults diagnosis in analog circuits is presented in this paper.The proposed method extracts the original signals from the output terminals of the circuits under test(CUT) by a data acquisition board.Firstly,the phase deviation value between fault-free and faulty conditions is obtained by fitting the sampling sequence with a sine curve.Secondly,the sampling sequence is organized into a square matrix and the spectral radius of this matrix is obtained.Thirdly,the smallest error of the spectral radius and the corresponding component value are obtained through comparing the spectral radius and phase deviation value with the trend curves of them,respectively,which are calculated from the simulation data.Finally,the fault location is completed by using the smallest error,and the corresponding component value is the parameter identification result.Both simulated and experimental results show the effectiveness of the proposed approach.It is particularly suitable for the fault location and parameter identification for analog integrated circuits. 展开更多
关键词 Index Terms--Analog circuits parameter identification phase deviation soft-fault diagnosis spectral radius.
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Study on the rupture characteristics of the overlaying soil with soft interlayer due to fault bedrock dislocation
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作者 赵雷 李小军 霍达 《Acta Seismologica Sinica(English Edition)》 EI CSCD 2006年第5期563-568,共6页
In this paper, the rupture characteristics of the overlaying soil with soft interlayer were studied by plane-strain finite element method. From the results, it can be shown that the existence of soft layer separates r... In this paper, the rupture characteristics of the overlaying soil with soft interlayer were studied by plane-strain finite element method. From the results, it can be shown that the existence of soft layer separates rupture process of the overlaying soil into two phases. The depth of a buried soft interlayer will influence the rupture process and the rupture range of the overlaying soil. The deeply buried soft interlayer would bring about a wider range of surface failure. In addition, the thickness of the soft layer also has effect on the rupture process and rupture range of the overlaying soil. 展开更多
关键词 fault soft interlayer overlaying soil finite element method
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Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter 被引量:4
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作者 余伶俐 蔡自兴 +1 位作者 周智 奉振球 《Journal of Central South University》 SCIE EI CAS 2012年第5期1249-1257,共9页
To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptiv... To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space. 展开更多
关键词 fault detection and diagnosis particle filter fuzzy logic hard fault soft fault
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基于一维卷积神经网络和Soft-Max分类器的风电机组行星齿轮箱故障检测 被引量:22
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作者 李东东 王浩 +3 位作者 杨帆 郑小霞 周文磊 邹胜华 《电机与控制应用》 2018年第6期80-87,108,共9页
将卷积神经网络引入风机故障检测领域,设计了一种一维卷积神经网络的结构,并和Soft-Max分类器相结合构造了一种双层智能诊断架构。一维卷积神经网络用于行星齿轮箱数据的特征提取,Soft-Max分类器对提取的特征进行分类。与传统智能算法相... 将卷积神经网络引入风机故障检测领域,设计了一种一维卷积神经网络的结构,并和Soft-Max分类器相结合构造了一种双层智能诊断架构。一维卷积神经网络用于行星齿轮箱数据的特征提取,Soft-Max分类器对提取的特征进行分类。与传统智能算法相比,该方法具有训练样本少,可直接使用原始数据训练网络;计算效率高,可以适应实时诊断的需要。试验结果证明,该方法可以有效地诊断出不同工况下的行星齿轮箱中的齿轮故障。 展开更多
关键词 风力发电机 行星齿轮箱 卷积神经网络 故障诊断 soft-Max分类器
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Data-Driven Process Monitoring and Fault Tolerant Control in Wind Energy Conversion System with Hydraulic Pitch System 被引量:1
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作者 王凯 罗浩 +3 位作者 KRUEGER M DING S X 杨旭 JEDSADA S 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第4期489-494,共6页
Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators an... Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works. 展开更多
关键词 DATA-DRIVEN process monitoring(PM) fault tolerant control(FTC) soft sensor wind turbine
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NEW APPROACH TO EMULATE SEU FAULTS ON SRAM BASED FPGAS
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作者 Reza Omidi Gosheblagh Karim Mohammadi 《Journal of Electronics(China)》 2014年第1期68-77,共10页
Field Programmable Gate Arrays(FPGAs)offer high capability in implementing of complex systems,and currently are an attractive solution for space system electronics.However,FPGAs are susceptible to radiation induced Si... Field Programmable Gate Arrays(FPGAs)offer high capability in implementing of complex systems,and currently are an attractive solution for space system electronics.However,FPGAs are susceptible to radiation induced Single-Event Upsets(SEUs).To insure reliable operation of FPGA based systems in a harsh radiation environment,various SEU mitigation techniques have been provided.In this paper we propose a system based on dynamic partial reconfiguration capability of the modern devices to evaluate the SEU fault effect in FPGA.The proposed approach combines the fault injection controller with the host FPGA,and therefore the hardware complexity is minimized.All of the SEU injection and evaluation requirements are performed by a soft-core which realized inside the host FPGA.Experimental results on some standard benchmark circuits reveal that the proposed system is able to speed up the fault injection campaign 50 times in compared to conventional method. 展开更多
关键词 Field Programmable Gate Arrays(FPGAs) Single-Event Upset(SEU) fault injection soft-core Space radiation effects
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The Seismic Induced Soft Sediment Deformation Structures in the Middle Jurassic of Western Qaidamu Basin 被引量:4
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作者 LI Yong SHAO Zhufu +2 位作者 MAO Cui YANG Yuping LIU Shengxin 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2013年第4期979-988,共10页
Intervals of soft-sediment deformation structures are well-exposed in Jurassic lacustrine deposits in the western Qaidamu basin. Through field observation, many soft-sediment deformation structures can be identified, ... Intervals of soft-sediment deformation structures are well-exposed in Jurassic lacustrine deposits in the western Qaidamu basin. Through field observation, many soft-sediment deformation structures can be identified, such as convoluted bedding, liquefied sand veins, load and flame structures, slump structures and sliding-overlapping structures. Based on their genesis, soft-sediment deformation structures can be classified as three types: seismic induced structures, vertical loading structures, and horizontal shear structures. Based on their geometry and genesis analysis, they are seismic-induced structures. According to the characteristics of convoluted bedding structures and liquefied sand veins, it can be inferred that there were earthquakes greater than magnitude 6 in the study area during the middle Jurassic. Furthermore, the study of the slump structures and sliding- overlapping structures indicates that there was a southeastern slope during the middle Jurassic. Since the distance from the study area to the Altyn Mountain and the Altyn fault is no more than 10km, it can be also inferred that the Altyn Mountain existed then and that the AItyn strike-slip fault was active during the middle Jurassic. 展开更多
关键词 soft-sediments deformation structure sliding-overlapping structure paleoseismology AItyn strike-slip fault
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基于波形相似性的柔性互联配电网短路故障区段定位方法 被引量:2
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作者 陈继明 陈文淙 +2 位作者 仉志华 于馨玮 徐乾 《电力工程技术》 北大核心 2025年第1期104-114,共11页
由于柔性多状态开关(soft normal open point,SNOP)复杂的控制策略及其弱馈特性,传统配电网故障定位方法难以适用于柔性互联配电网(flexible distribution network,FDN)。因此,文中提出一种利用电流正序分量波形相似性进行FDN故障区段... 由于柔性多状态开关(soft normal open point,SNOP)复杂的控制策略及其弱馈特性,传统配电网故障定位方法难以适用于柔性互联配电网(flexible distribution network,FDN)。因此,文中提出一种利用电流正序分量波形相似性进行FDN故障区段定位的方法。首先,针对SNOP的典型控制策略,分析FDN的短路故障特征。其次,计算配电网中不同故障位置电流正序分量的Tanimoto系数,通过对比不同位置的电流正序分量波形相似性,构建FDN短路故障定位判据,并通过Teager能量算子(Teager energy operation,TEO)实现故障时刻的精确定位,利用智能配电终端(smart terminal unit,STU)传递信息。最后,通过建模仿真对所提方法进行分析验证,结果表明该方法能够对故障区段进行准确定位,不受故障位置、故障类型、过渡电阻、采样频率及通信延时等因素的影响,验证了该方法的可行性与有效性。 展开更多
关键词 柔性互联配电网(FDN) 柔性多状态开关(SNOP) 故障分析 波形相似性 故障定位 电流正序分量
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Toward a Multi-Hop, Multi-Path Fault-Tolerant and Load Balancing Hierarchical Routing Protocol for Wireless Sensor Network
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作者 Mokhtar Beldjehem 《Wireless Sensor Network》 2013年第11期215-222,共8页
This paper describes a novel energy-aware multi-hop cluster-based fault-tolerant load balancing hierarchical routing protocol for a self-organizing wireless sensor network (WSN), which takes into account the broadcast... This paper describes a novel energy-aware multi-hop cluster-based fault-tolerant load balancing hierarchical routing protocol for a self-organizing wireless sensor network (WSN), which takes into account the broadcast nature of radio. The main idea is using hierarchical fuzzy soft clusters enabling non-exclusive overlapping clusters, thus allowing partial multiple membership of a node to more than one cluster, whereby for each cluster the clusterhead (CH) takes in charge intra-cluster issues of aggregating the information from nodes members, and then collaborate and coordinate with its related overlapping area heads (OAHs), which are elected heuristically to ensure inter-clusters communication. This communication is implemented using an extended version of time-division multiple access (TDMA) allowing the allocation of several slots for a given node, and alternating the role of the clusterhead and its associated overlapping area heads. Each cluster head relays information to overlapping area heads which in turn each relays it to other associated cluster heads in related clusters, thus the information propagates gradually until it reaches the sink in a multi-hop fashion. 展开更多
关键词 Wireless Sensor Network Protocol Design Hierarchical soft Clustering SELF-ORGANIZATION fault-TOLERANCE fault-Resilience ENERGY-EFFICIENCY Load Balancing Scalability Ambient Intelligence
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基于残差收缩卷积和GSoP注意力机制的旋转机械故障诊断
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作者 刘保罗 李晨 +1 位作者 聂雅琳 王国勇 《振动与冲击》 北大核心 2025年第13期277-287,共11页
针对强背景噪声与变载荷工作环境下,振动信号有效特征难以提取,故障诊断准确率低、泛化能力差的问题,提出了一种结合残差收缩卷积和全局二阶池化(global second-order pooling,GSoP)注意力机制的旋转机械故障诊断方法。该方法通过软阈... 针对强背景噪声与变载荷工作环境下,振动信号有效特征难以提取,故障诊断准确率低、泛化能力差的问题,提出了一种结合残差收缩卷积和全局二阶池化(global second-order pooling,GSoP)注意力机制的旋转机械故障诊断方法。该方法通过软阈值滤波技术与多通道、多尺度卷积相结合,构建残差收缩卷积,并在软阈值滤波基础上加入注意力因子,以抑制不相关特征并增强有效特征。此外,利用高阶统计建模思想,在残差收缩卷积层之后引入GSoP注意力机制,通过高层信道特征图的二阶统计信息提升模型判别性特征的提取能力。最后,利用凯斯西储大学轴承数据集和康涅狄格大学的齿轮箱数据集进行测试试验,所提方法在6 dB信噪比条件下分别实现了98.84%和99.41%的诊断准确率,在变噪声和变负载条件下,诊断性能均优于对比组模型。试验结果表明,所提方法在复杂工作环境下具有较好的故障识别能力和泛化能力。 展开更多
关键词 故障诊断 旋转机械 软阈值 全局二阶池化(GSoP)
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基于在线软标签的元学习轴承故障诊断方法
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作者 陶洁 陈贺文 +1 位作者 赵志磊 邱海文 《湖南工程学院学报(自然科学版)》 2025年第1期42-49,共8页
在极端小样本下,元学习故障诊断模型容易陷入过拟合,导致轴承故障诊断的准确率下降.基于此,提出一种基于在线软标签的元学习轴承故障诊断模型(Online Soft Label Metalearning,OSLM).首先将轴承原始振动信号作为卷积神经网络的输入;然... 在极端小样本下,元学习故障诊断模型容易陷入过拟合,导致轴承故障诊断的准确率下降.基于此,提出一种基于在线软标签的元学习轴承故障诊断模型(Online Soft Label Metalearning,OSLM).首先将轴承原始振动信号作为卷积神经网络的输入;然后在元学习网络框架下搭建卷积神经网络,以多任务的训练方式优化模型;最后,利用在线软标签算法统计模型预测的信息更新软标签,使用软标签指导神经网络训练.并将本文所提方法,在公开轴承数据集上进行试验,对跨工况条件和跨部件下的滚动轴承进行故障诊断实验.实验结果表明,本文所提方法相较其他方法具有更高的识别精度、更强的鲁棒性以及泛化性. 展开更多
关键词 小样本学习 元学习 在线软标签算法 轴承故障诊断
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SEDSR: Soft Error Detection Using Software Redundancy
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作者 Seyyed Amir Asghari Atena Abdi +2 位作者 Hassan Taheri Hossein Pedram Saadat Pourmozaffari 《Journal of Software Engineering and Applications》 2012年第9期664-670,共7页
This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use h... This paper presents a new method for soft error detection using software redundancy (SEDSR) that is able to detect transient faults. Soft errors damage the control flow and data of programs and designers usually use hardware-based solutions to handle them. Software-based techniques for soft error detection force less cost and delay to systems and do not change their configuration. Therefore, these kinds of methods are appropriate alternatives for hardware-based techniques. SEDSR has two separate parts for data and control flow errors detection. Fault injection method is used to compare SEDSR with previous methods of this field based on the new parameter of “Evaluation Factor” that takes in account fault coverage, memory and performance overheads. These parameters are important in real time safety critical applications. Experimental results on SPEC2000 and some traditional benchmarks of this field show that SEDSR is much better than previous methods of this field. SEDSR’s evaluation factor is about 50% better than other methods of this field. These results show its success in satisfaction of the existing tradeoff between fault coverage, performance and memory overheads. 展开更多
关键词 soft ERROR DETECTION Control Flow ERRORS Data ERRORS Evaluation Factor fault INJECTION
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基于图深度强化学习的有源配电网故障恢复方法
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作者 何小龙 高红均 +4 位作者 王仁浚 罗龙波 叶萌 黄媛 刘俊勇 《电网技术》 北大核心 2025年第10期4342-4352,I0090-I0094,共16页
配电网的拓扑结构变动频繁,负荷水平和分布式电源(distributed generator,DG)出力的不确定性使得运行场景愈加复杂多变。基于此,提出了一种基于图深度强化学习的有源配电网故障恢复方法。首先,考虑DG与负荷的时变性,构建起基于图注意力... 配电网的拓扑结构变动频繁,负荷水平和分布式电源(distributed generator,DG)出力的不确定性使得运行场景愈加复杂多变。基于此,提出了一种基于图深度强化学习的有源配电网故障恢复方法。首先,考虑DG与负荷的时变性,构建起基于图注意力网络(graph attention network,GAT)与柔性策略-评价(soft actor-critic,SAC)算法相结合的配电网故障恢复框架,介绍故障恢复方法及其算法原理。然后,建立面向配电网故障恢复的图深度强化学习模型,通过将GAT嵌入到SAC算法的前置神经网络来提高智能体对配电网运行状态和拓扑结构的感知能力,并创新性地引入无效动作掩盖机制以规避非法动作,通过智能体与环境进行交互,寻找最优开关动作控制策略,实现高渗透率DG接入下的故障恢复趋优学习。最后,在IEEE33节点和148节点算例进行验证,并与多种基线方法进行对比测试,所提方法可以实现最快毫秒级故障恢复,具有更加高效优越的恢复效果,在拓扑变动下的负荷供电率相较于基准模型提升了4%~5%。 展开更多
关键词 有源配电网 分布式电源 故障恢复 图注意力网络 柔性策略-评价 无效动作掩盖
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基于FSWT-DRSN的滚动轴承故障诊断方法
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作者 郭正刚 孙磊 +1 位作者 王贺 周正天 《机械设计与制造工程》 2025年第8期77-82,共6页
针对现有滚动轴承故障诊断算法在噪声背景下诊断精度低、稳定性差的问题,提出了基于FSWT-DRSN的滚动轴承故障诊断方法。首先,将一维振动信号进行频率切片小波变换,生成时频图。其次,结合软阈值、注意力机制和深度残差网络构建深度残差... 针对现有滚动轴承故障诊断算法在噪声背景下诊断精度低、稳定性差的问题,提出了基于FSWT-DRSN的滚动轴承故障诊断方法。首先,将一维振动信号进行频率切片小波变换,生成时频图。其次,结合软阈值、注意力机制和深度残差网络构建深度残差收缩网络(DRSN),实现对含噪样本自适应设置阈值,提升网络的抗噪性和稳定性。最后,将预处理过的时频图输入模型进行训练,实现滚动轴承故障分类。与现有的SVM、CNN和ResNet算法相比,FSWT-DRSN诊断精度更高且稳定性好,在噪声干扰下有出色的诊断性能。 展开更多
关键词 滚动轴承 故障诊断 深度残差收缩网络 软阈值化
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协调故障重构和智能软开关的柔性配电系统故障恢复策略 被引量:1
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作者 叶云 周飞 +2 位作者 张博 户政丽 杨晓东 《电力系统及其自动化学报》 北大核心 2025年第1期74-83,共10页
为增强柔性配电系统在极端灾害下的故障恢复能力,提出一种协调故障重构与智能软开关的柔性配电系统故障恢复方法。首先,建立考虑运行特性的智能软开关模型,包括功率输出模型和电压控制模型,并且引入包含智能软开关的虚拟潮流模型用以判... 为增强柔性配电系统在极端灾害下的故障恢复能力,提出一种协调故障重构与智能软开关的柔性配电系统故障恢复方法。首先,建立考虑运行特性的智能软开关模型,包括功率输出模型和电压控制模型,并且引入包含智能软开关的虚拟潮流模型用以判别智能软开关的控制模式;其次,提出协调联络开关、分段开关以及智能软开关的重构策略,建立了包含智能软开关的故障重构模型;然后,结合潮流约束、辐射状约束等约束建立柔性配电系统的故障恢复模型,采用二阶锥规划方法进行模型转化和求解;最后,在改进的IEEE 123节点测试系统上进行分析,验证所提模型的正确性和有效性。 展开更多
关键词 智能软开关 故障重构 柔性配电系统 负荷恢复 韧性提升
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融合多尺度时频域特征的电力电子电路软故障诊断
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作者 贺江涛 姜媛媛 《电机与控制应用》 2025年第7期788-799,共12页
【目的】直流电机系统中的电力电子器件在长期高频开关工作下易发生软故障。针对软故障诊断中存在的时频域特征融合不足、识别准确率低等问题,本文将长短期记忆(LSTM)网络和多尺度时频域交叉注意力机制(CAM)相结合,提出了一种基于LSTM-C... 【目的】直流电机系统中的电力电子器件在长期高频开关工作下易发生软故障。针对软故障诊断中存在的时频域特征融合不足、识别准确率低等问题,本文将长短期记忆(LSTM)网络和多尺度时频域交叉注意力机制(CAM)相结合,提出了一种基于LSTM-CAM-Transformer模型的故障诊断方法。【方法】首先,对采集到的故障信号进行预处理,采用霜冰优化算法(RIME)对变分模态分解(VMD)参数进行寻优,精准得到最优分解模态数K和惩罚因子α的组合,有效去除信号中的噪声与干扰成分。然后,提取各个本征模态函数的5维时域参数和5维频域参数,将其作为故障诊断的特征向量。最后,利用多尺度时频域CAM增强特征向量在时域和频域之间的信息交互,使模型进一步挖掘信号的时频域特征。【结果】将本文所提模型和其他四种模型进行对比以验证其优越性。试验结果表明,相较于其他四种模型,本文所提LSTM-CAM-Transformer模型收敛速度最快,稳定性和泛化性更强,且在诊断准确率、F1分数、损失值和召回率上的表现均优于其他四种模型。【结论】本文所提LSTM-CAM-Transformer模型通过融合基于RIME改进的VMD信号预处理策略与CAM时频特征增强机制,有效解决了传统方法中时频域特征融合不足的难题,为直流电机系统中电力电子设备的软故障诊断提供了一种高效可靠的新方法。 展开更多
关键词 变分模态分解 直流电机 长短期记忆网络 时频域特征 软故障诊断
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