A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Identification of Artificial Neural Network Models for Three-Dimensional Simulation of a Vibration-Acoustic Dynamic System

Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.

Topology identification for a class of complex dynamical networks using output variables

A problem of topology identification for complex dynamical networks is investigated in this paper. An adaptive observer is proposed to identify the topology of a complex dynamical networks based on the Lyapunov stability theory. Here the output of the network and the states of the observer are used to construct the updating law of the topology such that the communication resources from the network to its observer are saved. Some convergent criteria of the adaptive observer are derived in the form of linear inequality matrices. Several numerical examples are shown to demonstrate the effectiveness of the proposed observer.

Optimization-based topology identification of complex networks

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization- based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.

Synchronization-based approach for parameter identification in delayed chaotic network

This paper introduces an adaptive procedure for the problem of synchronization and parameter identification for chaotic networks with time-varying delay by combining adaptive control and linear feedback. In particular, we consider that the equations xi（t） （for i = r＋ 1, r＋2,... ,n） can be expressed by the former xi（t） （for i=1,2,...,r）, which is not the same as the previous equation. This approach is also able to track changes in the operating parameters of chaotic networks rapidly and the speed of synchronization and parameter estimation can be adjusted. In addition, this method is quite robust against the effect of slight noise and the estimated value of a parameter fluctuates around the correct value.

基于CatBoost算法的配电网分区拓扑辨识

含多分布式电源配电网的拓扑结构具有多样性与多变性,影响拓扑辨识的实时性和准确性。提出一种基于CatBoost算法的配电网分区拓扑辨识方法。构建结合拓扑分区的配电网拓扑辨识框架,采用区域开关状态矩阵描述拓扑结构,以进行物理上的辨识降维;提出基于CatBoost算法的特征选择与拓扑辨识方法,通过分区并行离线训练得到历史拓扑和未知拓扑的区域拓扑辨识CatBoost模型,通过在线应用得到实时的区域开关状态矩阵标签,形成配电网开关状态矩阵,实现系统拓扑辨识。配电网算例系统测试结果验证了所提方法的有效性。

基于神经网络的力矩补偿动力学参数辨识

针对机械臂刚柔耦合动力学模型拟合精度不高的问题,在分析动力学参数对各关节力矩影响的基础上,提出了一种基于神经网络力矩补偿的动力学参数辨识方法.首先,对动力学模型进行线性化分析,得到最小惯性参数集,设计机械臂激励轨迹,并采集各关节数据集.其次,搭建神经网络架构,对不同隐藏层数的神经网络模型的训练效果比较,证明了本文模型的准确性.将关节位置、速度、加速度数据集作为网络架构输入,经过神经网络学习后提取出各关节力矩.最后,对算法辨识出来的模型进行验证,以模型预测力矩的均方根误差为评判标准,关节拟合力矩结果表明,本文所用方法相较刚体逆动力学有更好的拟合精度,减少了关节摩擦等非线性因素对辨识实验的影响,得到更精准的动力学模型,对机械臂系统有更好的控制效果.

Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

Node dynamics and network topologies play vital roles in determining the network features and network dynamical behaviors.Thus it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

基于泥水平衡盾构掘进参数的PSO-BP神经网络掘进地层识别模型研究

为解决泥水平衡盾构机在掘进时无法准确地实时识别掘进地层的问题,以珠三角水资源配置工程为例,研究泥水平衡盾构机的盾构推力、掘进速度、刀盘转速、刀盘扭矩在不同地层下的变化规律,提出基于掘进参数的PSO-BP神经网络掘进地层识别方法,建立盾构推力、掘进速度、刀盘转速、刀盘扭矩4种掘进参数为输入集,地层编码为输出集的地层识别模型。工程数据的验证结果表明,该模型在珠三角水资源配置工程数据集上的掘进地层的识别准确率达99.07%,PSO-BP神经网络算法的识别准确率明显高于BP、RF、RBF、CNN等机械学习算法。

基于物理信息神经网络的牵引变流器直流支撑电容参数辨识方法

为了解决车载牵引变流系统直流支撑电容器故障预测问题,该文提出一种基于物理信息神经网络的直流支撑电容器参数辨识方法。该方法只需要利用直流环节预充电过程的直流支撑电容器两端电压及采样频率,无需拟合曲线,无需严格对齐时间轴就可以获得较为准确的电容参数辨识结果。与此同时,为了克服在采集数据时因条件所限造成的数据量稀疏与分布不均问题,该文利用循环一致性生成对抗网络算法增强数据,使该方法可以适用于同一拓扑下宽范围电容区间的电容容值预测,降低了模型训练要求。实验结果表明:在正常条件下,该方法的辨识相对误差约在1%以下,并且降低采样频率能够缓解信噪比对该方法的影响。该方法为解决直流支撑电容参数辨识问题提供了新思路。

基于智能电表数据的低压配电网拓扑与线路参数联合辨识

在低压配电网中,用户频繁地扩建和改接导致台账存在信息更新滞后、数据缺失等问题,难以获取当前运行状态下的拓扑及线路参数。针对无相角量测信息且含未知数量零注入功率“隐节点”的低压配电网,提出一种基于智能电表数据的低压配电网拓扑与线路参数联合辨识方法。基于低压配电网的电气特性以及辐射状网络的结构特点,推导出一种功率-电压比形式的线性逆潮流模型。通过线性回归求解得到阻抗距离矩阵,再利用无判定阈值形式的改进分组递归算法实现拓扑与线路参数的联合辨识。最后,在IEEE欧洲低压测试馈线和中国南京市某地区的实际低压配电网中对所提算法进行数值仿真,验证了所提算法的有效性。

一种基于拓扑结构变化的主动配电网自适应保护方法

随着逆变型分布式电源大量接入配电网,配电网运行方式灵活多变,保护方案的设计与保护装置的动作范围受拓扑变化的影响。在此背景下并结合工程实际,提出一种基于拓扑结构变化的主动配电网自适应保护方法。通过多点量测信息感知配电网的运行状态,利用电压量和电流量之间的相关性,对拓扑结构的变化进行识别。然后,根据拓扑结构的变化,形成新的关联矩阵,自适应调整系统的保护方案与参数。最后,通过仿真验证了所提出的方法有效性。

基于分区和滑动时间窗策略的配电网线路参数辨识

针对配电网线路参数辨识困难、不精确的问题,提出了一种基于分区和滑动时间窗策略的配电网线路参数辨识的方法。首先,使用线性解耦潮流模型最小二乘回归得到线路初始辨识参数。然后,将配电网分成若干个区域,并采用滑动时间窗策略,在每个时间窗中对每个区域内的线路参数通过高斯-牛顿法进行精确辨识。最后,对所有时间窗内的辨识结果进行离群值检测,综合各个时间窗的辨识结果得到最终的辨识值。所提方法提高了线路参数的辨识精度,并且可以避免精确辨识时选取到坏数据导致辨识结果偏离真实值。

基于μPMU量测的配电网自适应拓扑识别方法

针对现有拓扑识别方法存在的耗时长、量测冗余多、难以捕捉到网络变化等问题,提出了一种基于自适应退火算法的配电网拓扑识别方法。基于优化配置的微型同步相量量测单元(μPMU),通过增添校验装置,自动修正拓扑识别过程中不良数据、干扰噪声的影响;通过自适应控温、变异的退火方法,实现闭环反馈,并比较拓扑相异度以准确进行拓扑识别。算例分析结果表明,所提方法在不同的干扰、量测误差下均具有较高的识别准确率,实时性强且鲁棒性好。

一种可用于航天Boost拓扑电源参数辨识的数字孪生方法

电力电子变换器中元器件随时间退化会影响到航天电源的稳定性与可靠性。为确保航天电源的可靠性,并准确识别零部件状态,数字孪生依靠其非侵入性、数字化、高性价比、低风险、可视化与实时性的特点,可以解决电力电子变换器的故障趋势判断与状态检测的问题,从而保证航天电源的稳定性与可靠性。通过验证Boost拓扑电路的数字孪生模型,同时采用禁忌搜索算法以比较结果最小二乘误差为目标函数得到全局最优解,进行Boost拓扑电路参数辨识、实时监控。并且对比了100、500、1000算法迭代次数对结果的影响,随着迭代次数的增加,最终结果与实际值仅有最大0.96%的误差。验证该项技术可应用到航天电源的实时状态监控,以保证其可靠性。

应用随机森林与神经网络算法的足底软组织本构参数反演方法

目的 基于随机森林(random forest, RF)算法和反向传播(back propagation, BP)神经网络算法实现对足底软组织超弹性模型本构参数的预测,以提升本构参数获取方式的效率和准确性。方法 首先建立足底软组织球形压痕实验的有限元模型,并对球形压痕实验过程进行仿真,得到具有非线性关系的位移和压痕力的数据集。将数据集进行划分,得到训练集和测试集,分别对搭建好的RF和BP神经网络(BP neural network, BPNN)模型进行训练,通过实验数据对足底软组织本构参数进行预测。最后,引入均方误差(mean square error, MSE)和决定系数(R2)对模型的预测准确性进行评估,同时对比实验曲线验证模型的有效性。结果 利用RF和BPNN模型结合有限元仿真是确定足底软组织超弹性本构参数的有效、准确的方法。训练后的RF模型MSE达到1.370 2×10^(-3),R^(2)为0.982 9;BPNN模型MSE达到4.858 1×10^(-5),R^(2)为0.999 3。反求得到适用于仿真的足底软组织的超弹性本构参数,预测得到的两组本构参数的计算响应曲线与实验曲线吻合较好。结论 基于人工智能算法模型对足底软组织超弹性本构参数的预测精度很高,相关研究成果也可以应用于足底软组织其他力学特性的研究。同时,研究结果为足底软组织本构参数的获取提供新方法,有助于快速诊断足底软组织病变等临床问题。

基于二项熵和邻域节点间范德华力的关键节点识别方法

对节点重要性进行排序是复杂网络中识别关键节点的一种常用分析方法,分析网络中节点的重要性,有助于深入了解网络特性。在现有方法上为进一步提升节点评估精准度,引入二项熵概念来量化节点在网络中的重要性,通过邻域相似度衡量节点间的相互影响力,同时采用范德华力抽象节点之间的相互作用关系,提出一种基于二项熵和邻域节点间范德华力的关键节点识别方法,该方法从网络的整体信息流和相邻节点之间的位置和交互关系,综合考虑节点的局部和全局特征,并选取3个同类算法通过3个评价指标验证性能优劣,实验结果表明该算法对重要节点的判断具有良好的性能。

基于BP神经网络的降落伞气动力参数辨识

针对降落伞回收系统动力学仿真中的气动力参数不能直接测量的问题,建立了降落伞系统稳定下降阶段的六自由度动力学方程和运动学方程,确定了气动力的形式以及待辨识参数。在此基础上采用了两种基于反向传播(Back Propagation,BP)神经网络的气动力参数辨识方案,使用飞行状态数据训练神经网络直至收敛,得到待辨识的气动力参数模型。通过仿真算例验证了两种辨识方案的有效性和辨识模型的正确性,分别得到气动力参数辨识结果,并计算了性能评价指标。根据仿真结果从收敛速度、辨识精度等方面分析了两种辨识方案的效果,结果显示:两种辨识方案预测结果与预期结果均吻合较好,但是双BP神经网络方法更具有优势。结果证明BP神经网络方法对未来工程中的空投试验数据辨识具有潜在应用价值。

基于机器学习的钻井液流变参数智能识别方法

钻井液流变性是钻井液流动和变形的特性,对于携带与悬浮岩屑、提高钻进速度至关重要,准确掌握钻井液流变参数是保证井眼清洁与高效钻进的前提。提出一种基于卷积神经网络(Convolu-tionalNeuralNetwork,CNN)的钻井液流变参数智能识别方法,通过磁力搅拌产生稳定的钻井液流动图像,利用多种数据增强方法增加图像数量并建立数据库,增强模型的鲁棒性和泛化能力。优化AlexNet卷积神经网络算法,构建钻井液流变参数识别模型。将数据库划分为训练集:验证集:测试集=7:2:1,对训练集进行迭代训练并通过验证集调整参数获得最佳拟合模型。此外,运用混淆矩阵、卷积核可视化技术和类激活技术(Gradient-weighted Class Activation Mapping,Grad-CAM)对模型进行多方位评估。结果表明:(1)钻井液流变参数识别模型对钻井液塑性黏度测试的宏精确率为95.2%,宏召回率为94.7%,宏F1值为0.95。(2)对钻井液表观黏度测试的宏精确率为91.6%,宏召回率为91.5%,宏F1值为0.90。(3)利用卷积核可视化技术和Grad-CAM对特征提取进行可视化处理,发现钻井液波纹形状和大小会影响模型流变参数识别准确度。(4)室内测试结果表明,该模型的测试误差为±2 mPa·s,在设计允许范围以内,具有较高的识别精确度和稳定性。所提出的钻井液流变参数实时智能识别方法可为安全、快速和准确地进行钻井液流变性测试提供智能化技术思路。

基于热网络分区等效策略的Si/SiC混合器件耦合热参数辨识方法

由硅(Si)绝缘栅双极型晶体管(IGBT)与碳化硅(SiC)金属氧化物半导体场效应晶体管(MOSFET)并联构成的混合器件可打破单一Si基器件和SiC基器件的局限性,实现损耗和成本间的有效均衡。结温估计对于Si/SiC混合器件的可靠运行至关重要,然而,目前针对热监测的研究往往需满足热稳态平衡和功率损耗可测两大条件,在实际变流器中实用性较低。基于此,该文以零输入响应法为切入点,首先,对Si/SiC混合器件热结构特性进行分析,详细阐述零输入响应法直接应用于混合器件时所带来的高阶热约束条件难以表征的问题;其次,通过对SiIGBT与SiC MOSFET进行热网络分区处理实现模型的降阶,构建出壳温降温曲线时间常数与热参数之间的约束关系,提出了基于热网络分区等效思想的混合器件耦合热参数辨识方法,所提方法简化了功率损耗测量步骤与热平衡态条件,在并联器件耦合热参数辨识研究中具有简单、通用、流程化高的应用优势;最后,通过实验验证了该文所提方法的准确性和有效性。