A coherency function model of ground motion at base rock corresponding to strike-slip fault

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.

GREEN FUNCTION在齿轮箱故障诊断中的运用

为了找出齿轮箱故障的定量规律，通过对齿轮箱的振动信号的测试及分析，发现ＧＲＥＥＮＦＵＮＣＴＩＯＮ对齿轮箱故障的诊断行之有效，并且成功地找出了Ｇ１０、Ｇ１０′等齿轮箱故障的定量诊断参数．

DC System Modeling and AC/DC System Harmonic Calculation under Asymmetric Faults

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.

Online Fault Diagnosis Method Based on Nonlinear Spectral Analysis

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.

Fault tolerant control based on stochastic distribution via RBF neural networks

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.

Additive Fault Tolerant Control Applied to Delayed Singularly Perturbed System

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.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

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.

Crustal structure along the Zhenkang-Luxi deep seismic sounding profile in Yunnan derived from receiver functions

The crustal thicknesses and the Poisson’s ratios under the seismic stations can be calculated by receiver function method with H-κ stacking effectively. But the stacking results are affected to some extent by the average crustal P-wave velocity. To eliminate this effect and get more accurate crustal structure along the Zhenkang-Luxi deep seismic sounding profile which lies in Yunnan Province, we calculate the receiver functions from the teleseismic events recorded by 11 temporary stations as well as 5 permanent ones along the profile and carry out the stacking with Vp obtained from the profile in this study. Our study shows that the crustal thicknesses along the Zhenkang-Luxi profile range from 34.8 km to 41.8 km with an average of 39 km. The crust is thicker in the middle part of the profile and thinner in both sides in general. Dramatic changes of crustal thickness about 3 km are detected across both the Lancangjiang fault and the Xiaojiang fault, which implies that these faults cut through the Moho. The lowest Poisson’s ratio under the stations is 0.22 and the highest is 0.27 with the mean of 0.25, which is lower than the global average value 0.27 in the continental crust. It suggests that most of the crust along the profile lacks mafic component, but contains more felsic substance. The low Poisson’s ratio also indicates that there is no satisfying condition for partial melting. We deduce that the material flow in the middle-lower crust in the southeastern margin of the Tibetan plateau may occur only in the north region of 24°N.

基于连续小波卷积神经网络的轴承智能故障诊断方法

传统故障诊断方法存在特征提取有限和故障检测不准确的问题,为此提出新的轴承智能故障诊断方法.构建连续小波卷积层取代卷积神经网络(CNN)中的初始卷积层,用于提取轴承数据的初级特征;使用增强ACON激活函数处理提取的振动信号;设计新的计算空间,提高CNN的整体自适应性.在凯斯西储大学轴承数据集上开展滚动轴承故障诊断方法对比实验.结果表明,与传统基于CNN、快速傅里叶变换-CNN、长短时记忆CNN故障诊断方法相比,所提方法的故障诊断精度分别提高了7.45、4.46和1.53个百分点,CNN的收敛速度更快.在不同工况的泛化任务中,所提方法的平均准确率为99.64%,准确性和泛化能力良好.

Fault Diagnosis of Machine Based on Fuzzy Reliability Theory

According to life analysis in reliability theory, certain diagnosis rules can be used to diagnose machines' faults. On this basis, considering the indefiniteness in machine working states, the accurate diagnosis rule was extended to fuzzy diagnosis rule by using basic concepts and methods of fuzzy mathematics. The formulas of fault probability under different conditions were deduced. In the end, an example is given and the results of two methods were compared.

Scheme of optimal fault detection for linear discrete time-varying systems with delayed state

This paper deals with the problem of the optimal fault detection (FD) for linear discrete time-varying (LDTV) systems with delayed state and l(2)-norm bounded unknown input. The novelty lies in the designing of an evaluation function for the robust FD. The basic idea is to directly construct an evaluation function by using a weighted l(2)-norm of the measurement output, which achieves an optimal trade-off between the sensitivity to fault and the robustness to l(2)-norm bounded unknown input. To avoid complex computation, a feasible solution is obtained via the recursive computation by applying the orthogonal projection. It is shown that such an evaluation function provides a unified scheme for both the cases of unknown input being l(2)-norm bounded and jointly normal distribution, while a threshold may be chosen based on a priori knowledge of unknown input. A numerical example is given to demonstrate the effectiveness of the proposed method.

Adaptive Learning Rate Optimization BP Algorithm with Logarithmic Objective Function

This paper presents an improved BP algorithm. The approach can reduce the amount of computation by using the logarithmic objective function. The learning rate μ(k) per iteration is determined by dynamic optimization method to accelerate the convergence rate. Since the determination of the learning rate in the proposed BP algorithm only uses the obtained first order derivatives in standard BP algorithm(SBP), the scale of computational and storage burden is like that of SBP algorithm,and the convergence rate is remarkably accelerated. Computer simulations demonstrate the effectiveness of the proposed algorithm

Fault Detection for a One-Dimensional Wave PDE System

In this paper, we design a fault diagnosis scheme for a class of variable coefficient wave equation (an overhead crane system), which is composed of an observer and its output error is treated as residual signal. When the system is in a healthy state, the output residual signal decays exponentially. Due to the existence of external disturbance, the residual is not zero in the state without fault. Therefore, we further design a reasonable threshold which is based on the upper bound of the residual dynamics and external disturbance to reduce the influence of external disturbance and determine whether the system fault occurs. The convergence properties of partial differential equation (PDE) observer and residual signal are analyzed by Lyapunov stability theory. Finally, the effectiveness of this fault diagnosis is illustrated by simulation and we can judge whether this overhead crane system fails by this fault diagnosis scheme.

Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

In this paper,a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network.The sensor fault and the system input uncertainty are assumed to be unknown but bounded.The radial basis function (RBF) neural network is used to approximate the sensor fault.Based on the output of the RBF neural network,the sliding mode observer is presented.Using the Lyapunov method,a criterion for stability is given in terms of matrix inequality.Finally,an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

基于电磁时间反演P范数判据的配电网故障定位

目前电磁时间反演(electromagnetic time reversal,EMTR)多应用在单一线路故障定位,且现有判据在高阻抗接地情况下效果不理想。针对上述问题,基于EMTR故障定位原理和均匀传输线理论推导了传播过程中线路故障信号与测量信号的传递函数,根据传递函数的相关性提出了P范数判据。利用ATP-EMTP搭建10 kV配电网线路,对比了2范数与P范数判据在复杂配电网中的定位性能,并验证了所提判据在混合配电网线路的适用性。最后,分析了配电网发生低阻抗及高阻抗接地故障下P范数判据的鲁棒性。仿真结果表明,该方法在过渡电阻高达3 kΩ的情况下能准确定位,且定位精度高,受噪声、故障类型和采样频率的影响小。

一种新型5G专网组网方案及实现

本文分析了目前5G专网的现状及存在问题,提出一种新的组网方案,并分析其部署场景,介绍具体实现过程。这种新的组网方案克服目前专网建设的难点,推动5G专网更好融入千行百业。

基于电磁时间反演的VSC-HVDC系统架空线-电缆混合线路故障定位方法

针对传统故障定位方法无法应用于架空线-电缆混合直流线路的问题,提出了一种架空线-电缆混合直流线路的故障定位方法,该方法采用先确定故障点所在区段、后在区段中定位的原理。利用故障附加网络的直流分量定义了故障区段判别函数,通过该判别函数在不同区段发生故障时数值不同实现故障区段的判定。然后,分析了电磁时间反演理论在故障定位中的可行性,分析确定了故障特征谐波。利用线路两端特征谐波的电流行进波作为时间反演电路的电流源,设置与原线路拓扑结构镜像的线路,利用时间反演电流源与镜像线路构建时间反演电路。在此基础上,根据时间反演的能量聚焦特性列写故障区段内的故障定位方程并实现区段内故障定位。利用PSCAD/EMTDC搭建的电压源型换流器高压直流输电(VSC-HVDC)系统仿真证明了所提方法能实现线缆混合直流输电线路的故障定位。

A new method to calculate near-field radiation excited by heterogeneous fault rupture

This paper derives from the representation theory the formula for calculating the radiation excited by heterogeneous fault rupture based on box-like discretization scheme. Preliminary validation indicates that our algorithm has very high computation precision and efficiency; therefore, it is a very practical tool to investigate strong ground motion problems. Additionally, the equations given in this study can also be used to invert the fault rupture process.

商用车电控转向系统的发展现状与趋势

商用车转向系统的电控化是实现商用车节能化和智能化的必由之路,为此商用车转向系统从传统的液压助力转向系统发展到以电液耦合助力转向系统为代表的电控转向系统。本文将对商用车电控转向技术进行综述,从电控转向系统的典型构型、智能转向控制和功能安全设计3个方面对商用车电控转向技术的研究进行梳理,总结电控转向技术的重点研究领域和未来发展方向。梳理总结发现:(1)在系统构型方面,商用车电控转向系统以电液耦合转向助力技术为主,着重于实现商用车智能转向功能,同时兼顾节能化需求;商用车电动助力转向系统是未来理想的电控转向技术方案。(2)在智能控制方面,为了适应商用车智能化的发展,转向执行控制着重于解决液压系统所造成的非线性与时滞性问题以及轨迹跟随过程中的横向动力学控制难题;辅助驾驶功能着重于克服液压系统的非线性导致的转向力矩不连续以及人机控制权的分配;自动驾驶功能着重于提升安全性与燃油经济性。(3)在系统功能安全设计方面,商用车电控转向系统着重于设计符合功能安全标准的故障诊断与容错控制策略。