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.

Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency

The accurate estimation of the rolling element bearing instantaneous rotational frequency(IRF) is the key capability of the order tracking method based on time-frequency analysis. The rolling element bearing IRF can be accurately estimated according to the instantaneous fault characteristic frequency(IFCF). However, in an environment with a low signal-to-noise ratio(SNR), e.g., an incipient fault or function at a low speed, the signal contains strong background noise that seriously affects the effectiveness of the aforementioned method. An algorithm of signal preprocessing based on empirical mode decomposition(EMD) and wavelet shrinkage was proposed in this work. Compared with EMD denoising by the cross-correlation coefficient and kurtosis(CCK) criterion, the method of EMD soft-thresholding(ST) denoising can ensure the integrity of the signal, improve the SNR, and highlight fault features. The effectiveness of the algorithm for rolling element bearing IRF estimation by EMD ST denoising and the IFCF was validated by both simulated and experimental bearing vibration signals at a low SNR.

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.

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。

深度子领域自适应网络电机滚动轴承跨工况故障诊断

针对实际生产中旋转机械工况变化引起状态监测数据分布差异及获取待诊断样本标签困难问题,提出多尺度子领域自适应模型(MSDAM)的跨工况下滚动轴承故障诊断方法。首先,以原始振动信号作为输入,无需信号预处理及人工特征参数提取;其次,搭建多尺度卷积神经网络将已知标签样本和待诊断样本特征迁移到同一子空间,捕获具有细粒度信息的多尺度公共特征;然后,以不同的故障类型来划分相关子域,并通过局部最大均值距离(LMMD)来完成子域的适配,有效削弱不同工况同类故障特征的分布差异;最后,在三个数据集的多个迁移任务上进行试验验证。结果证明,所提MSDAM的跨工况故障诊断性能优于关注全局领域适配的迁移学习方法。

基于软阈值降噪的脉冲卷积神经网络轴承故障诊断方法

针对工业场景下滚动轴承信号易受噪声干扰,导致故障诊断准确率低和稳定性差的问题,本文提出一种基于软阈值降噪的脉冲卷积神经网络诊断方法。该方法使用软阈值滤波去噪,运用带时间标签的卷积层处理二维信号,增强动态特征提取能力。同时,通过引入IF和LIF神经元实现对时域和频域信息的联合编码,并采用替代梯度法进行端到端训练。实验结果显示,在信噪比为6dB时,所提方法的诊断准确率达100%,在信噪比为-6dB时诊断准确率达77.33%,优于其他常用方法,表明所提方法在噪声下具有良好的诊断效果和稳定性。

考虑多种故障场景的柔性互联低压配电网故障恢复方法

针对多变压器柔性互联台区变压器N-1、N-2故障的场景,提出了多设备协调的故障恢复方法。首先,梳理了变压器N-1、N-2故障场景的特点和各场景下配电站内的母联开关和台区间互联的SOP协调的故障恢复流程;其次,考虑网络运行约束、故障恢复过程中设备能力约束等,建立了以负荷加权恢复量最大为目标的柔性互联配电网故障恢复模型,并将其转化为二阶锥求解;最后,在21节点+21节点柔性互联的典型低压配电网拓扑中验证了所提方法的效果,证明了其可以有效提升变压器故障后重要负荷保供电的能力。

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.

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。

深井软岩高应力巷道过超大落差断层破碎带围岩控制技术

针对超大断层带围岩破碎软弱、自承载力低、应力场复杂和动力扰动等不良地质条件,提出了围岩改性+局部强化+主被动联合支护为主体的断层治理新技术,即超前注浆掩护,小循环注浆加固,反底拱局部底板强化等联合支护技术。通过数值模拟对不同支护方案进行比选,获得巷道最优支护参数。数值模拟验结果表明:随着支护强度的提高,锚杆索受力趋于均匀,承压拱范围逐步变大,承载能力增强,喷浆层最大剪力呈减小趋势,最大弯矩先减小后增大;围岩塑性区范围逐渐减小,塑性区分布位置由两帮、底板逐渐演变为两帮,面积减少了约75%。工业性试验结果表明,巷道围岩28 d后趋于稳定,最大顶板下沉量约15 mm,底鼓量约25 mm,帮部移近量约35 mm,围岩稳定性控制较好。

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.

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.

富水断层带隧道变形力学机制及双梯度注浆NPR补偿对策

云南他白依隧道位于青藏高原东麓,穿越多条断层构造带,其围岩软弱破碎,遇水强度骤降。原设计方案下围岩存在米级大变形,出现了拱架扭曲、仰拱隆起和突泥涌水等灾害。针对富水断层带隧道围岩破碎难题,首先通过矿物成分分析、点荷载试验及地应力测试,分析了富水断层带隧道变形力学机制及其转化,通过未注浆、常规注浆及双梯度注浆的围岩承载力试验,确定采用双梯度注浆工艺进行NPR锚索锚固;然后,通过数值模拟及现场试验,得出了最佳锚索数目;最后,通过现场应用,基于监测数据验证了双梯度注浆及高预紧力长短NPR锚索支护体系的可靠性。研究结果可为类似富水断层带隧道的大变形灾害控制提供参考。

冗余捷联惯导软故障检测方法研究

针对冗余捷联惯导系统在软故障检测上仍然存在实时性低、检测性能易受环境影响的问题,提出了一种APV/FASPRT算法。首先根据硬件冗余配置构造奇偶空间,对奇偶残差执行SPRT算法,引入了渐消因子与周期重置提高对当前残差信息的跟踪速度;其次通过APV算法检测故障结束时刻以重置渐消SPRT并提供敏感轴信息;最后针对工程常用的四、六陀螺冗余配置提出了一种基于可容性故障的检测阈值确定方法以增强故障检测的稳定性。仿真结果表明,该算法在软故障检测上分别比GLT、SPRT、APV方法平均检测延迟减少了50.59%、70.21%、2.32%、平均虚警率降低了69.31%、99.33%、64.77%,在增强了软故障检测的实时性的同时减少了无故障时刻的虚警率。