Fault detection based on H_∞ states observer for networked control systems

The influence of random short time-delay to networked control systems （NCS） is changed into an unknown bounded uncertain part. Without changing the structure of the system, an Hoo states observer is designed for NCS with short time-delay. Based on the designed states observer, a robust fault detection approach is proposed for NCS. In addition, an optimization method for the selection of the detection threshold is introduced for better tradeoff between the robustness and the sensitivity. Finally, some simulation results demonstrate that the presented states observer is robust and the fault detection for NCS is effective.

Qualitative analysis for state/event fault trees using formal model checking

A state/event fault tree(SEFT)is a modeling technique for describing the causal chains of events leading to failure in software-controlled complex systems.Such systems are ubiquitous in all areas of everyday life,and safety and reliability analyses are increasingly required for these systems.SEFTs combine elements from the traditional fault tree with elements from state-based techniques.In the context of the real-time safety-critical systems,SEFTs do not describe the time properties and important timedependent system behaviors that can lead to system failures.Further,SEFTs lack the precise semantics required for formally modeling time behaviors.In this paper,we present a qualitative analysis method for SEFTs based on transformation from SEFT to timed automata(TA),and use the model checker UPPAAL to verify system requirements’properties.The combination of SEFT and TA is an important step towards an integrated design and verification process for real-time safety-critical systems.Finally,we present a case study of a powerboat autopilot system to confirm our method is viable and valid after achieving the verification goal step by step.

Research on information technology of state monitoring and fault prediction for mechatronics system

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.

Reexamining the Seismological Implications of the Present-day Stress State of the Yingxiu-Beichuan Fault after the Wenchuan Earthquake

The present-day stress state of the Yingxiu-Beichuan fault after the Wenchuan earthquake was re-estimated using measured in-situ stress data obtained after the Wenchuan earthquake. The results reveal that the gradient coefficients of principal stresses versus depth decrease from south to north along the Yingxiu-Beichuan fault, revealing that the stress level decreases from south to north. The consistency between the present-day stress levels and surface ruptures generated during the earthquake indicates that the accumulated tectonic stress beneath the Yingxiu-Beichuan fault before the Wenchuan earthquake was relieved in form of surface ruptures. This resulted in the stress remaining high in the southern section of the Yingxiu-Beichuan fault but relatively low in the northern section. Abnormal high pore pressure conditions and an extremely low frictional coefficient play important role in the interpretation of the stress field adjustment and seismic events observed after the Wenchuan earthquake along this fault, according to the estimation results using the Coulomb frictional-failure theory incorporating frictional coefficients ranging from 0.4 to 1.0. To accurately estimate the seismological hazard of the Yingxiu-Beichuan fault by analyzing fault instability using the Coulomb frictional-failure theory, much attention should be focused on the pore pressure conditions and the evolution state of the frictional coefficient under the present-day stress state.

Robust fault detection in linear systemsbased on full-order state observers

A parametric approach to robust fault detection in linear systems with unknown disturbances is presented. The residual is generated using full-order state observers （FSO）. Based on an analytical solution to a type of Sylvester matrix equations, the parameterization of the observer gain matrix is given. In terms of the design degrees of freedom provided by the parametric observer design and a group of introduced parameter vectors, a sufficient and necessary condition for fullorder state observer design with disturbance decoupling is then established. By properly constraining the design parameters according to this proposed condition, the effect of the disturbance on the residual signal is also decoupled, and a simple algorithm is developed. The presented approach offers all the degrees of design freedom. Finally, a numerical example illustrates the effect of the proposed approach.

Effect of Poisson’s ratio on stress state in the Wenchuan M_S8.0 earthquake fault

The Wenchuan Ms8.0 earthquake occurred on the Longmenshan fault which inclines at a dip angle exceeding 60 degrees. Since most thrust earthquakes occur on faults with dip angles of about 30 degrees, it is enigmatic why the Wenchuan earthquake occurred on such a steep fault. In this study we use a simple finite element model to investigate how the stress state in the fault changes with the variation of Poisson＇s ratio. The results show that, with the Poisson＇s ratio in the fault increasing, the magnitudes of the principal stresses increase and the maximum Shear stress decrease, and, especially, the angle between the maximum principal stress and the fault plane decreases, which will enhance the driving force to overcome the frictional resistance on the fault. The increase of Poisson＇s ratio in the fault may be an important factor to affect the occurrence of the fault earthquakes with large angles between maximum principal stress and fault plane.

基于固态断路器主动注入式直流故障测距方法

随着直流配电网的快速发展与深化应用,准确可靠的故障测距技术对直流故障检修和恢复至关重要。该文提出一种基于固态断路器(SSCB)主动注入式直流故障测距方法。首先,利用SSCB开断主动注入不同脉冲宽度的信号,并检测注入脉冲首末端和反射波首末端的时间差作为注入波形传播时间间隔,能够减小采样频率带来的误差。然后,提取电缆线路电压线模量作为检测量,削弱正负极线路之间的耦合作用,获得稳定波速。最后,基于小波变换原理提出改进自适应模极大值方法来检测脉冲首末端时刻,减小噪声和过渡电阻对测距精度的影响。仿真结果表明,基于SSCB主动注入式测距方法在直流配电网单极接地和极间短路故障情况下均能够实现准确且快速的定位。

储能锂电池系统综合管理研究进展

锂电池系统是新型储能系统的重要组成部分,电池管理技术对确保电池系统的安全、高效运行和延长使用寿命具有重要意义。从电池系统的模型、状态、故障、一致性、热管理等层面出发,综述了锂电池管理的研究进展。在电池模型方面,分析总结了电池在电、热、力等特性的建模及高效计算方法。对于电池的状态估计,总结了基于模型、数据驱动以及两者相结合的状态估计方法以及各自的优缺点,分析了构建数据驱动与电池领域知识融合框架的未来发展前景。在电池故障方面,分析了电池系统不同故障特征、类型、触发机制和诊断方法,对电池系统在早期故障预警、故障检测灵敏度提升等方面进行了展望。对于电池系统的一致性,总结分析了在均衡拓扑和均衡策略两个方面的研究现状,并对电池重构在均衡、快充、能量利用提升、故障隔离等方面的应用进行了分析。在电池热管理方面,分析了高温冷却以及低温加热的方法,并对热管理系统的优化设计和控制策略进行了总结,对开发高效换热、轻量化以及低能耗的先进热管理系统进行了探讨。此外,对电池管理系统的新技术和应用场景进行了概述,分析了数字孪生技术以及储能综合管理在电池系统上的应用,为未来电池管理的发展提供了参考。

基于CWT-RES34的风电机组叶片裂纹状态评估

为有效进行风电机组叶片运行时的裂纹状态评估,提出一种基于连续小波变换(Continue Wavelet Transform,CWT)和残差神经网络(Residual Networks,ResNet)结合的叶片裂纹状态评估方法。首先对叶片加速度振动信号做CWT后生成二维彩色时频图像,然后将图像分别作为训练集和测试集,使用34层ResNet进行训练和诊断,最后选取天津某风电场提供的1.5 MW风力发电机作为研究对象,根据其样本数据将叶片故障程度按照裂纹长度和宽度分为健康、轻微、中等、严重、危险5种状态,评估平均准确率高达98.23%,方法的有效性和可行性得到验证。

基于混合强化学习的主动配电网故障恢复方法

针对高比例新能源接入的配电网故障恢复问题,提出一种基于混合强化学习的主动配电网故障恢复方法。首先,以故障损失最小为恢复目标、配电网安全运行要求为约束条件,构造主动配电网的故障恢复模型;其次,建立用于故障恢复的强化学习环境,根据状态空间和动作空间特点,提出一种混合强化学习方法,该方法使用竞争架构双深度Q网络算法处理离散动作空间,进行开关动作;然后,使用深度确定性策略梯度算法处理连续动作空间,调节电源出力;最后,通过IEEE33节点系统仿真实验验证所提方法的可行性和优越性。

组合式有源传感器状态监测与容错控制研究

针对传统传感器状态监测方法准确性不高的问题,提出一种组合式有源传感器状态监测与容错控制方法。该方法分为2个部分:前一部分进行传感器状态监测,包括状态信号去噪处理、小波包能量熵特征提取以及利用参数优化后的概率神经网络识别传感器健康状态3个步骤;后一部分设计容错控制模型,以状态监测结果为参考,切换控制策略,实现容错控制。实验结果表明:与其他3种传统方法相比,在该方法控制下容错控制误差在±1 r/min,波动较小,控制效果更优。

基于荷电状态一致性的电池组双层动态均衡方法

针对储能系统中电池组充放电过程中能量利用率以及系统运行安全性较低的问题,提出考虑SOC一致性的电池组双层动态均衡方法。首先,采用耦合电感与Flyback变换器搭建均衡系统双层架构,建立电池组端电压、均衡电流及占空比间的关联特性。为提高电池组的供能可靠性,系统引入故障切除功能,通过改变开关阵列导通状态实现故障电池组的快速切除;其次,考虑增补电池组剩余容量较大问题,利用传统最值法改进的双层极值法,以荷电状态(stage of charge,SOC)作为均衡目标变量,对增补电池组进行快速放电均衡;最后,设计充放电及静置均衡实验,对比传统最值法,分析常态及故障切除后电路的均衡速度与均衡效率。结果表明,提出的双层均衡方法可以将均衡速度提升约10%,且故障切除后电路的均衡效率最高可达95%以上。

地震亚失稳过程中前兆异常演化的综合解释——以2014年鲁甸6.5级地震为例

文中以2014年鲁甸6.5级地震为例,在亚失稳实验及理论指导下,基于震前地震活动及地球物理观测资料并结合地震成核数值模拟结果,综合分析前兆异常时空演化与亚失稳过程的关系,首次提供了一个可明确佐证亚失稳阶段震源区成核、震源区附近协同化过程持续加剧的典型震例和观测事实。研究结果显示,依据大区域强震活动及流动重力观测,可判定鲁甸地震前研究区已处于高应力状态。在高应力背景判定的基础上,依据地震活动及地球物理观测前兆异常,可粗略判定鲁甸地震亚失稳过程可能起始于震前7、8个月,最突出的现象或判定指标是由震中附近小地震活跃所表征的断层应力状态由积累为主向释放为主的转变,以及由定点地球物理观测异常数量显著增加所表征的断层运动协同化现象,从地震成核的角度与成核后期核心弱化区扩张过程有关。之后至主震发生,还有2个时间节点需要关注:一是震前4、5个月,定点地球物理观测异常空间分布范围自震中附近向外围的明显扩展,显示断层变形的加速协同化;二是震前2个月之后,震中附近小地震活动开始减弱、微震活动及定点地球物理异常出现向震中的迁移收缩,与地震成核过程核心弱化区扩展之后的收缩过程相关联。

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.

基于BN-AHP的装甲车辆动力系统故障状态评估

装甲车辆复杂传统部件的损伤能否及时被发现,这关系到整车战备或作战能力,系统的故障状态评估是至关重要的。将贝叶斯网络模型结合云模型理论,建立云贝叶斯网络模型,针对4个不同工况的装甲车辆进行故障状态评估。在获取贝叶斯网络初始节点时更多是依靠专家经验,往往会带来很大的误差,导致条件概率偏差过大,采用证据理论/层次分析法来优化专家经验,确定各个节点的条件概率;将层次分析法转化所得的条件概率值代入到云贝叶斯网络模型中,经过计算可以得到不同损毁等级的概率。将云贝叶斯网络模型计算结果与其他状态评估方法结果进行对比分析,结果表明,所采用的计算方法较其他方法在可靠性和准确性方面有所提高。

利用EEMD和深度置信网络的滚动轴承故障诊断方法

结合滚动轴承振动信号的非线性特点,开展基于EEMD和深度置信网络(deep belief network,DBN)的滚动轴承故障诊断方法研究。应用EEMD对信号进行分解后,结合相关系数与峭度分析完成信号重构,构建加速度-速度矩阵作为DBN模型输入,实现滚动轴承不同故障类型及其损伤程度的诊断预测。研究过程中对模型泛化能力、层特征提取能力、模型分析结果和分析效率等方面进行了对比分析,对原始数据、加速度-速度矩阵、相图作为DBN模型输入的诊断效果进行了对比分析,同时进行了SVM、XGboost、ResNet、DBN不同网络模型的对比分析。分析结果表明,利用EEMD进行信号处理并构建加速度-速度矩阵作为DBN模型输入可以有效实现滚动轴承的不同故障类型和故障损伤程度的诊断,诊断平均准确率达到97.23%。此模型可为轴承状态监测与智能故障诊断技术的工程应用提供参考。

基于主动方波激励检测锂离子电池早期内短路

锂离子电池内部短路(ISC)的早期检测对于避免潜在安全风险至关重要。现有的故障检测方法在检测早期短路时准确性较差,且需要大量的数据。以18650型锂离子电池为研究对象,通过在电池充电过程中施加微小的电流激励信号,借助极化压升与激励压升,发现锂离子电池在短路时压升异常规律。该方法在荷电状态(SOC)较低时效果更明显,对120Ω以内的短路检测有效,且适用范围可根据采样精度调配,可用于锂离子电池的常规短路检测。

多期构造应力控制走滑断控储层发育机理与差异性研究——以塔里木盆地顺北地区为例

塔里木盆地顺北油气田发育典型走滑断控缝洞型储层。区别于受原始沉积相带控制的基质孔洞、受岩溶改造的洞穴等储集类型,走滑断控缝洞型储层的形成主要受断裂活动期构造应力导致的破裂作用控制。为研究走滑断裂多期活动背景下构造应力控制的断控储层发育机理与分布规律,综合应用野外、岩心、测录井、地震及钻井动态等资料,开展不同走滑断裂、同一走滑断裂沿走向不同部位、垂向不同层系断控储层发育特征表征,并结合应力场数值模拟手段开展多期构造应力恢复,预测断控储层发育的主要时期与分布规律。结果表明,走滑断裂活动期不同分段内部应力状态差异显著,其中拉分段内部以张应力为主,主要派生张性裂缝;压隆段内部以挤压应力为主,派生裂缝类型多样。顺北地区勘探目的层奥陶系一间房组顶面断控裂缝主要在加里东中期Ⅲ幕及加里东晚期—海西早期发育,海西中—晚期及之后裂缝基本不发育,且滑移距大的顺北18号断裂带较顺北1号断裂带派生裂缝开度、密度更高。走滑断控储层具有“簇状”结构特征,断裂活动期不同分段内部应力状态控制储层结构差异,拉分段“空腔”多,以“双簇”结构为主,核带规模大,压隆段核带结构多,以“多簇”结构为主,分割性强;走滑断裂活动期应力强度控制储集空间类型与规模,小型断裂储集空间以裂缝为主,大型断裂带发育核带结构,断控储层规模与断裂活动强度呈正相关关系;断控裂缝发育主要受断裂早期活动控制,晚期由于地层埋深增大,岩石不易破裂,新派生裂缝发育较少。

不同稳态工况对同步调相机单相短路后暂态温升的影响

同步调相机可在系统故障时为特高压直流换流站提供暂态无功支持,以保持系统电压稳定,防止直流换相失败,但调相机的高无功输出也会导致其温升增加,运行能力受限。系统故障前同步调相机的初始工况是影响其暂态温升和运行能力的一个重要因素,为研究系统低电压时调相机暂态温升与初始工况之间的关系,该文将调相机的电磁场、温度场与电网模型耦合,计算调相机不同稳态工况下的定转子损耗和温度分布;以线路单相短路故障引起的系统低电压为例,研究调相机初始工况对暂态温升的影响。在考虑各结构件最大容许温度的条件下,揭示调相机单相短路持续时间随初始工况的变化规律。研究结果为提升调相机在系统低电压下的暂态运行能力提供技术支持。