Soft Fault Diagnosis of Analog Circuit Based on Particle Swarm Optimization

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.

Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

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.

An adaptive particle filter for soft fault compensation of mobile robots

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.

Diagnosis of soft faults in analog integrated circuits based on fractional correlation

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.

Method for Analog Circuit Soft-Fault Diagnosis and Parameter Identification Based on Indictor of Phase Deviation and Spectral Radius

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.

Study on the rupture characteristics of the overlaying soil with soft interlayer due to fault bedrock dislocation

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.

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

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。

Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter

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.

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

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。

考虑电压支撑的含SOP柔性配电网故障关联矩阵可靠性计算方法

配电网柔性化已成为重要发展趋势,智能软开关作为取代传统联络开关的柔性互联装置,其高响应速度及电压支撑作用有助于提升供电可靠性,但现有配电网可靠性计算方法大多忽略了故障后节点电压影响,无法适用于含智能软开关的柔性互联配电网可靠性计算.对此,本文基于故障关联矩阵可靠性计算理论,提出一种考虑电压支撑的柔性互联配电网可靠性计算方法,实现了柔性互联配电网可靠性指标的快速解析计算.首先,构建供电路径矩阵以表征故障影响范围和节点故障后供电恢复能力;然后,形成故障后支路传输功率向量以及节点电压矩阵,求取故障关联矩阵进而得到可靠性指标;最后,基于中压配电网典型算例进行测试验证.结果表明,本文所提柔性配电网可靠性计算方法得到的可靠性指标与枚举法相同,能够保证精度,同时本方法以矩阵代数运算代替故障枚举过程中的重复性网络搜索过程,搜索时间减少了87%,显著提升计算效率.

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

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。

基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断

目的为了解决双向DC-DC电力变换器的软故障诊断精度不高的问题,方法提出基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型。首先,分析双向DC-DC电力变换器中电容、电感和MOSFET管的故障机理,通过仿真实验模拟各元件失效后变换器的输出电气参数变化,从而确定变换器不同元件故障时对应的故障特征参数;其次,构建改进的LSTM-SVM双向DC-DC电力变换器故障诊断组合模型,在LSTM中添加Mogrifier门机制,提高LSTM提取时间序列原始数据中微弱特征的能力;最后,由于传统LSTM的末端分类器为Softmax,其主要解决单一元件诊断问题,变换器故障类型较多,维数较高,所以采用麻雀搜索算法优化的SVM代替原有的Softmax函数,对LSTM输出的数据进行故障分类,提高故障诊断的准确率。设置双向DC-DC电力变换器充放电两种状态下,包含电解电容、电感和MOSFET单双管故障在内的24组故障,分别采用本文构建的改进的LSTM-SVM和原始的LSTM-SVM双向DC-DC变换器故障诊断模型进行诊断。结果结果表明,改进的LSTM-SVM故障诊断模型诊断准确率平均值为99.71%,原始的LSTM-SVM故障诊断模型诊断准确率平均值为88.48%,改进的LSTM-SVM故障诊断模型对各元件的故障诊断正确率均高于原始的LSTM-SVM故障诊断模型的。结论基于改进LSTM-SVM的双向DC-DC电力变换器故障诊断模型实现了对双向DC-DC电力变换器中的电解电容、电感和MOSFET单双管故障的准确诊断。

NEW APPROACH TO EMULATE SEU FAULTS ON SRAM BASED FPGAS

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.

NPR锚索对跨断层软岩大变形隧道控制技术研究

为探究NPR锚索对东马场跨断层软岩大变形隧道控制效果,首先对NPR锚索进行静力拉伸试验,验证其恒阻大变形的能力和具有良好的抵御外力的效果;随后开展NPR锚索对东马场隧道围岩变形控制的三维物理模型试验。试验现象和数据表明:在开挖过程中,隧道围岩也没有出现较大垮落和坍塌,NPR锚索对软岩隧道围岩有较好的支护效果。在加载完成后,NPR锚索对非断层处的隧道围岩最大位移约为14 mm,断层处的围岩最大位移约为21 mm,隧道区域所受应力分布的不规律性,导致隧道围岩出现挤压变形,位移曲线呈振荡状。在NPR锚索的支护下,围岩应力最大发生在左拱肩,围岩临空面3 cm处切应力最大(0.3 MPa),隧道未出现大规模的不对称破坏现象,隧道总体呈现出“小塌而不垮”,变形总体可控。

The Seismic Induced Soft Sediment Deformation Structures in the Middle Jurassic of Western Qaidamu Basin

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.

Toward a Multi-Hop, Multi-Path Fault-Tolerant and Load Balancing Hierarchical Routing Protocol for Wireless Sensor Network

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.

具有不确定滑转的轮式移动机器人轨迹跟踪控制方法

轮式移动机器人在松软复杂地形条件下容易发生车轮滑转,影响轨迹跟踪精度.为提高机器人在该情况下的轨迹跟踪准确性,提出了一种基于容错控制的自适应滑转补偿控制器.首先,考虑车轮滑转率建立了四轮移动机器人运动学模型,并构建了相应的轨迹跟踪误差模型;然后,根据容错控制方法设计了滑转补偿项,并基于Lyapunov稳定性理论证明了自适应滑转补偿控制器的稳定性,实现了对由车轮滑转引起的速度误差的准确补偿;最后,利用四轮移动机器人进行了两种工况下的实验验证.结果表明,设计的控制器能够使移动机器人在未知的车轮滑转率条件下准确跟踪理想路径,并且相较于常规控制器有更强的鲁棒性和更高的跟踪精度.

基于软传感器的冷水机组传感系统故障诊断及补偿策略

冷水机组与建筑内温度控制密切相关,其内部传感器故障会直接影响机组正常运行,也会导致能源使用效率下降。因此,传感网络故障诊断与补偿策略研究对保障冷水机组可靠稳定运行和优化室内舒适性具有十分重要的意义。本文提出1种基于软传感器的冷水机组传感系统故障诊断及补偿方法。首先,建立了基于故障诊断软传感器的温度观测器,并将其与实测温度序列相结合,利用残差检测传感器故障。同时,本文提出1种综合虚警率和故障检测率的新故障阈值构建方法,实现了传感器故障的精确诊断。在此基础上,通过多传感器互检定位故障传感器。其次,针对检出故障传感器,构建补偿软传感器,利用该传感器历史正常值估计当前值,对故障值进行补偿。最后,通过实验验证了本文提出的故障诊断及补偿方法的有效性,实验结果表明故障诊断软传感器的平均虚警率为1.29%,平均故障诊断率为97.18%。故障补偿软传感器的平均绝对误差和均方根误差分别为0.0306和0.0480。

考虑可平移负荷和智能软开关的主动配电网故障恢复方法

在配电网发生故障后,主动配电网能够实现用户侧参与功率的实时供需平衡,满足故障后的负荷恢复。用户参与配电网的调度也是提升故障后负荷恢复比例的途径之一,可平移负荷具有较强的可控性,有效提升了主动配电网的灵活性,智能软开关等新兴可控设备的接入,也使负荷的快速供电成为可能。本文建立了考虑可平移负荷和智能软开关的主动配电网故障恢复模型,并考虑了负荷的重要性系数。通过改进的IEEE 33节点算例进行仿真分析,构建单一故障、多点故障、短时故障的场景,验证了本文所提出方法的有效性以及可控装置对故障后负荷恢复量的提升。

SEDSR: Soft Error Detection Using Software Redundancy

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.