共查询到20条相似文献，搜索用时 234 毫秒
1.
针对大型电网设备中的故障信号稳定性较差,并且信号之间的关联性较弱,导致不能有效检测故障的问题,提出了一种引入估计推理模型的大型电网设备故障检测方法。系统依据贝叶斯网络的学习和概率分析能力,在传统的神经网络诊断模型中,引入一种推理估计模型,以电网设备中的测速机、脉冲发生器、传感元件的高频故障信号为基础,估计模型运算电网设备的故障概率,配合边缘化算对数据结构中的故障信息进行表达。仿真实验说明,算法可以解决电网故障信号的随机性特征,准确检测出电网设备中的故障信号。 相似文献
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大型云计算联合服务器中故障节点的快速挖掘模型构建可以实现对云服务器故障的准确定位和检测。传统方法中采用协议堆栈对节点进行约束与管理,达到故障节点快速挖掘的目的,然而该算法在Sink节点位置部署考虑欠好,在通信传输中很容易相邻节点信道间频谱主瓣重叠,故障节点挖掘性能不好。针对这一问题,提出一种基于正交通信信道载波均衡的云计算联合服务器故障节点快速挖掘算法,建立故障节点信息融合模型,进行特征分析,在信息融合过程中,组成新的云计算联合服务器接收端和发射端故障节点定位训练序列,构建基于OFDM系统的等效基带故障节点挖掘模型,通过正交通信信道载波均衡实现云服务器故障节点的快速挖掘。仿真结果表明,该算法在复杂云计算环境下,能实现对故障节点的准确定位,挖掘性能较高,检测概率较高,优越于传统模型。 相似文献
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研究使用虚拟噪声补偿技术的自适应kalman滤波算法。首先对实际系统模型中的误差部分进行虚拟噪声补偿,然后通过一般自适应kalman滤波算法相结合,使改进的自适应kalman滤波算法在带有模型误差和噪声统计特性误差的前提下,能够在线估计观测随机误差的噪声特性。并编制仿真软件,验证改进算法的可行性。 相似文献
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研究使用虚拟噪声补偿技术的自适应kalman滤波算法。首先对实际系统模型中的误差部分进行虚拟噪声补偿,然后通过一般自适应kalman滤波算法相结合,使改进的自适应kalman滤波算法在带有模型误差和噪声统计特性误差的前提下,能够在线估计观测随机误差的噪声特性。并编制仿真软件,验证改进算法的可行性。 相似文献
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结合分析UMHexagon S和经典运动估计算法,本文提出了一种基于运动信息自适应的运动估计算法。该算法利用宏块的时空相关性实现对静止块的判定,并对其直接停止搜索;依据块的运动类型来自适应选择起始点和模板,对于中、小运动块跳过大模板粗搜索直接进入小模板细搜索。实验结果表明,基于运动信息自适应的运动估计算法的搜索精度接近于UMHexagon S,但是搜索速率优于H.264标准中已有的快速运动估计算法。 相似文献
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提出了一种将人工神经网络理论应用于单相自适应重合闸中的方法,建立了一个3层的BP网络模型。利用MATLAB进行了大量仿真实验,验证了该方法在瞬时性故障与永久性故障识别中的可行性。 相似文献
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提出一种基于启发式云计算的多源资源访问特征最小方差估计算法,构建多源资源访问的云计算Cloud-P2P融合模型,采用遗传算法对Cloud-P2P融合模型中多源资源访问特征进行信息提取,给出多源资源信息访问特征状态,采用自适应全局概率搜索,自主对整个所需搜索范围进行搜索以及优化,实现对多源资源访问特征的最小方差估计,提高访问性能。仿真结果表明,算法特征信息提取精度高,方差估计准确,通过启发式云计算对多源资源信息系统访问特征的最小方差准确估计,利用了众多闲置的普通用户终端节点上蕴含的巨大的计算和存储资源,可灵活设置备份规模,鲁棒性高,性能优越。 相似文献
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《黑龙江科技信息》2020,(25)
复杂环境下航迹快速规划是智能飞行器控制的一个重要课题,本文通过多约束条件下智能飞行器航迹快速规划进行了研究,提出了解决飞行器由于自身定位系统受到限制和误差校正点可能失效情况下最优航迹规划的方法。通过运用基于多标号修正法(Multi-Label Correcting Algorithm)的快速最短路算法(SPFA),建立了带转弯约束的双目标航迹规划模型。该模型对由飞行器飞行环境随时间发生动态变化带来的校正误差有一定的适应性。通过仿真模拟,模型有良好的推广性,计算时间短且稳定。本研究为智能飞行器的快速航迹规划及控制,乃至误差校正点的位置设立提供了一定的参考。 相似文献
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This paper is concerned with integrated event-triggered fault estimation (FE) and sliding mode fault-tolerant control (FTC) for a class of discrete-time Lipschtiz nonlinear networked control systems (NCSs) subject to actuator fault and disturbance. First, an event-triggered fault/state observer is designed to estimate the system state and actuator fault simultaneously. And then, a discrete-time sliding surface is constructed in state-estimation space. By the use of a reformulated Lipschitz property and delay system analysis method, the sliding mode dynamics and state/fault error dynamics are converted into a unified linear parameter varying (LPV) networked system model by taking into account the event-triggered scheme, actuator fault, external disturbance and network-induced delay. Based on this model and with the aid of Lyapunov–Krasovskii functional method, a delay-dependent sufficient condition is derived to guarantee the stability of the resulting closed-loop system with prescribed H∞ performance. Furthermore, an observed-based sliding mode FTC law is synthesized to make sure the reachability of the sliding surface. Finally, simulation results are conducted to verify the effectiveness of the proposed method. 相似文献
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《Journal of The Franklin Institute》2022,359(16):9357-9375
In this paper, the distributed adaptive fault estimation issue using practical fixed-time design is investigated for attitude synchronization control systems. A distributed fault estimation observer is proposed based on the fixed-time technique. Meanwhile, a novel fixed-time adaptive fault estimation algorithm is also constructed to guarantee convergence rate and improve estimation rapidity. The fault estimation error is uniformly ultimately bounded and is practically fixed-time stable, which converges to the neighborhood of the origin in a fixed time. Finally, simulation results of an attitude synchronization control system are presented to verify the effectiveness of proposed techniques. 相似文献
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电网故障诊断的基本思想是根据保护动作原理将故障诊断问题表示为0-1规划问题。为了保证电网故障诊断的准确性和实时性,提出了一种改进的人工鱼群算法——二进制人工鱼群算法。分析了人工鱼群群聚行为和追尾行为最优方向的前进速度。并在此基础上与遗传算法、粒子群算法和量子免疫算法作了对比分析。结果表明:追尾行为最优方向的前进速度优于群聚行为,二进制人工鱼群算法综合性能优于遗传算法、粒子群算法和量子免疫算法。研究表明二进制人工鱼群算法具有收敛速度快、种群规模小和搜索能力强的特点。 相似文献
14.
Gianfranco Gagliardi Francesco Tedesco Alessandro Casavola 《Journal of The Franklin Institute》2018,355(14):6710-6745
This paper illustrates the derivation of a linear parameter varying (LPV) model approximation of a turbocharged Spark-Ignition (SI) automotive engine and its usage in designing a model-based fault detection and isolation (FDI) scheme. The LPV approximation is derived from a detailed nonlinear mathematical model of the engine on the basis of the well known Jacobian approach. The resulting LPV representation is then exploited for synthesizing a bank of LPV-FDI Luenberger observers. Each observer is in charge of detecting a particular class of fault and is designed for having low sensitivity to all other exogenous inputs so as to allow an effective fault isolation. The adopted FDI scheme is gain-scheduled and exploits a set of engine variables, assumed to be measurable on-line, as a scheduling parameters. The goodness of the LPV approximation of the engine model and the effectiveness of the LPV-FDI architecture are demonstrated by several numerical simulations. 相似文献
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《Journal of The Franklin Institute》2023,360(7):4626-4639
This study considers state and fault estimation for a switched system with a dual noise term. A zonotopic and Gaussian Kalman filter for state estimation is designed to obtain state estimation interval in the presence of both stochastic and unknown but bounded (UBB) uncertainties. The switching state and fault state of the system are distinguished by detecting whether the system measurement date is within the bounds of its predicted output. Once the switched time is detected in the system, the filter zonotopic and Gaussian Kalman functions are initialized. Once the fault time is detected, a zonotopic and Gaussian Kalman filter-based fault estimator is constructed to estimate the corresponding faults. Finally, a numerical simulation is presented to demonstrate the accuracy and effectiveness of the proposed algorithm. 相似文献
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This paper proposes a new sliding mode observer for fault reconstruction, applicable for a class of linear parameter varying (LPV) systems. Observer schemes for actuator and sensor fault reconstruction are presented. For the actuator fault reconstruction scheme, a virtual system comprising the system matrix and a fixed input distribution matrix is used for the design of the observer. The fixed input distribution matrix is instrumental in simplifying the synthesis procedure to create the observer gains to ensure a stable closed-loop reduced order sliding motion. The ‘output error injection signals’ from the observer are used as the basis for reconstructing the fault signals. For the sensor fault observer design, augmenting the LPV system with a filtered version of the faulty measurements allows the sensor fault reconstruction problem to be posed as an actuator fault reconstruction scenario. Simulation tests based on a high-fidelity nonlinear model of a transport aircraft have been used to demonstrate the proposed actuator and sensor FDI schemes. The simulation results show their efficacy. 相似文献
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The goal of the interval observers is to deal with the large but bounded uncertainties and disturbances by determining certain interval (upper and lower estimates) for the system states at each time instant. The mean of the interval that should be minimized can be considered as the point-wise estimate whereas the interval width provides the admissible deviation from that value. Thus, an interval estimation error bound is provided at any time instant that converges to zero in the absence of exogenous signals. Interval observers can be used in a wide range of applications because of its reliable uncertainties propagation such as robust control of linear and non-linear systems, fault detection and isolation, anti-disturbance controller design and so on. This paper presents some of the basic concepts and recent results obtained to design interval observers for uncertain systems like discrete-time, continuous-time, Linear Parameter Varying (LPV) systems and multiagent/interconnected systems. In addition, it also presents a brief discussion of the main approaches with some future recommendations. 相似文献
18.
Yun Feng Yaonan Wang Jun-Wei Wang Han-Xiong Li 《Journal of The Franklin Institute》2021,358(2):1656-1672
Detection and estimation of abnormalities for distributed parameter system (DPS) have wide applications in industry, e.g., battery thermal fault diagnosis, quality monitoring of hot-rolled strip laminar cooling process. In this paper, the abnormal spatio-temporal (S-T) source detection and estimation problem for a linear unstable DPS is first studied. The proposed methodology consists of two steps: first, an abnormality detection filter (ADF) which generates a residual signal for abnormality detection in the time domain is constructed using pointwise measurement; Then, an adaptive Luenberger-type PDE observer including an adaptive estimation algorithm is designed and triggered only when an alarm raises from the ADF. Theoretic analysis based on the spatial domain decomposition approach is presented to show the convergence of the estimation errors. Finally, an illustrative example is presented to show the performance of the proposed method. 相似文献
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Parameter fault detection and estimation of a class of nonlinear systems using observers 总被引：1，自引：0，他引：1
The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method. 相似文献
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《Journal of The Franklin Institute》2014,351(12):5565-5581
This paper is concerned with the identification problem of linear parameter varying (LPV) time-delay systems. Due to inherent nonlinearity, the industrial processes are often approximately described by an LPV model constructed by synthesizing multiple local models. Time-delay is commonly experienced in industrial processes and it can be parameter varying or constant in the process model. The multiple model identification of LPV systems with parameter varying or constant time-delay is formulated in the scheme of the expectation-maximization (EM) algorithm and the parameter varying property and the time-delay property of the process are handled simultaneously. The irrigation channel example and high purity distillation column example are used to present the effectiveness of the proposed method. 相似文献