Review:Recent Developments in Dynamic Load Identification for Aerospace Vehicles Considering Multi⁃source Uncertainties

The determination of the dynamic load is one of the indispensable technologies for structure design and health monitoring for aerospace vehicles.However,it is a significant challenge to measure the external excitation directly.By contrast,the technique of dynamic load identification based on the dynamic model and the response information is a feasible access to obtain the dynamic load indirectly.Furthermore,there are multi-source uncertainties which cannot be neglected for complex systems in the load identification process,especially for aerospace vehicles.In this paper,recent developments in the dynamic load identification field for aerospace vehicles considering multi-source uncertainties are reviewed,including the deterministic dynamic load identification and uncertain dynamic load identification.The inversion methods with different principles of concentrated and distributed loads,and the quantification and propagation analysis for multi-source uncertainties are discussed.Eventually,several possibilities remaining to be explored are illustrated in brief.

Simulating Study of Dynamic Load Spectra Identification Method of Machinery in Cepstrum Domain

Based on the platform of Matlab and the theory of digital signal processing, we propose a method in the cepstrum domain for dynamic load spectra identification of machinery. We demonstrate that the dynamic load spectra can be identified from the response signal of the system, based on cepstra. An ARMA model is built based on the harmonic retrieval by high-order spectra. The coefficients of a Green function are determined and the window width can be estimated. Finally the effectiveness of the method is validated by simulation results.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

Inverse Load Identification in Stiffened Plate Structure Based on in situ Strain Measurement

For practical engineering structures,it is usually difficult to measure external load distribution in a direct manner,which makes inverse load identification important.Specifically,load identification is a typical inverse problem,for which the models(e.g.,response matrix)are often ill-posed,resulting in degraded accuracy and impaired noise immunity of load identification.This study aims at identifying external loads in a stiffened plate structure,through comparing the effectiveness of different methods for parameter selection in regulation problems,including the Generalized Cross Validation(GCV)method,the Ordinary Cross Validation method and the truncated singular value decomposition method.With demonstrated high accuracy,the GCV method is used to identify concentrated loads in three different directions(e.g.,vertical,lateral and longitudinal)exerted on a stiffened plate.The results show that the GCV method is able to effectively identify multi-source static loads,with relative errors less than 5%.Moreover,under the situation of swept frequency excitation,when the excitation frequency is near the natural frequency of the structure,the GCV method can achieve much higher accuracy compared with direct inversion.At other excitation frequencies,the average recognition error of the GCV method load identification less than 10%.

EXPERIMENT RESEARCH ON DYNAMIC LOAD IDENTIFICATION TECHNOLOGY BASED ON GENERALIZED ORTHOGONAL POLYNOMIAL THEORY (GOPT)

A dynamic load identification model of structural system based on the gener-alized orthogonal polynomial theory is provided, and the least Square discrete algorithm foridentifying the dynamic load is supplied. The main key is that the convolution relationsbetween the input and output of the system in time domain are transformed into linear oP-erators in generalized orthogonal domain. The new theory is fully tested and verified bythe dynamic analysis l 'modal test and dynamic load identification teSt of a simulation speci-men- It is shown that the method has some advantages, such as the simple dynamic cali-bration test, the high identification accuracy, especially for the transient load with shortsampling. These are very useful in engineering applications.

Dynamic Load Identification for Structures with Variable Stiffness Based on Extended Kalman Filter

We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.

Study on Identification of Inductive-Motors Load Partition Based on Coherence

A new inductive motors load equivalence algorithm based on coherence is proposed in this paper. In order to partite motors load rapidly and accurately, fuzzy c-means clustering along with particle swarm optimization (PSO-FCM) algorithm is proposed to identify coherent motors base on its physical essence of fuzziness. The merits of PSO algorithm are independent to initial value and convergent to optimum value rapidly, and the validity function is constructed to assess clustering validity. The test on IEEE 39-Bus System is presented to evaluate the effectiveness of the new algorithm, the membership matrix definite not only coherence group of motors but also correlation value of coherence between motors. The algorithm can be used to partite motor load based on coherency in dynamic equivalence with power system operating on different modes.

Modal Parameters Identification of A Real Offshore Platform From the Response Excited by Natural Ice Loading

This paper investigates the possibility of utilizing response from natural ice loading for modal parameter identification of real offshore platforms.The test platform is the JZ20-2 MUQ jacket platform located in the Liaodong Bay,China.A field experiment is carried out in winter season,as the platform is excited by floating ices.The feasibility is demonstrated by the acceleration response of two different segments.By the SSI-data method,the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments.The estimated information from both segments is almost identical,which demonstrates that the modal identification is trustworthy.Furthermore,by taking the Jacket platform as a benchmark,the numerical performance of five popular time-domain EMA methods is systematically compared from different viewpoints.The comparisons are categorized as:(1)stochastic methods versus deterministic methods;(2)high-order methods versus low-order methods;(3)data-driven versus covariance-driven stochastic subspace identification methods.

Identification of Critical Lines in Power System Based on Optimal Load Shedding

Based on risk theory, considering the probability of an accident and the severity of the sequence, combining N-1 and N-2 security check, this paper puts forward a new risk index, which uses the amount of optimal load shedding as the severity of an accident consequence to identify the critical lines in power system. Taking IEEE24-RTS as an example, the simulation results verify the correctness and effectiveness of the proposed index.

Damage Identification in Simply Supported Bridge Based on Rotational-Angle Influence Lines Method

To locate and quantify local damage in a simply supported bridge, in this study, we derived a rotational-angle influence line equation of a simply supported beam model with local damage. Using the diagram multiplication method, we introduce an analytical formula for a novel damage-identification indicator, namely the diff erence of rotational-angle influence linescurvature(DRAIL-C). If the initial stiff ness of the simply supported beam is known, the analytical formula can be effectively used to determine the extent of damage under certain circumstances. We determined the effectiveness and anti-noise performance of this new damage-identification method using numerical examples of a simply supported beam, a simply supported hollow-slab bridge, and a simply supported truss bridge. The results show that the DRAIL-C is directly proportional to the moving concentrated load and inversely proportional to the distance between the bridge support and the concentrated load and the distance between the damaged truss girder and the angle measuring points. The DRAIL-C indicator is more sensitive to the damage in a steel-truss-bridge bottom chord than it is to the other elements.

基于盲源分离的工业谐波源负荷分类识别方法

针对传统谐波源辨识方法无法实现工业用户谐波源具体类型的非侵入式识别的问题,该文提出一种基于盲源分离的工业谐波源负荷分类识别方法。该方法仅依据工业用户进线处电压、电流数据即可实现谐波源负荷具体类型的非侵入式识别。首先,从负荷等值阻抗模型入手,建立工业用户多负荷等值阻抗并联电路模型;其次,采用集合经验模态分解与奇异值分解相结合的方法确定构成用户进线处监测点综合等值阻抗信号的源阻抗信号数目;然后,采用快速独立分量分析实现将谐波源负荷等值阻抗信号从综合负荷等值阻抗信号中分离;最后,将分离出谐波源负荷等值阻抗信号频率特征与典型谐波源负荷进行匹配,进而实现分类识别。仿真与实测实验结果均表明,所提方法能够准确识别工业用户所含多种谐波源负荷的具体类型,具有较好的可行性和实用性。

基于工作模态辨识的高速飞行器运输约束动载荷辨识技术研究

在运输振动测量数据基础上,本文提出了基于工作模态辨识的高速飞行器运输约束动载荷辨识方法。首先,详细介绍了ERA(Eigensystem Realization Algorithm)环境激励模态辨识方法的理论和载荷辨识时域方法的理论;其次,给出了飞行器结构动力学建模方法;再次,提出了基于工作模态辨识的高速飞行器运输约束动载荷辨识计算工作流程,详细分析了各操作步骤;最后,通过算例验证了方法的可行性,其中,基于振动测量数据,采用环境激励模态辨识方法辨识各时刻的模态,包括模态频率和模态振型,再利用振动响应的模态叠加原理和模态正交理论,获取各时刻飞行器低阶模态的响应,再结合模态剪力和模态弯矩进行动载荷识别,采用载荷识别方法获取了高速飞行器约束点的约束载荷时域历程。

结构动载荷识别研究进展

作用于工程结构上的动载荷由于环境等限制难以通过直接测量的方式获取,基于动响应信息间接识别或重构动载荷已成为一种十分有效的途径。动载荷识别经过数十年的发展,已经形成了一系列行之有效的方法。本文总结了动载荷识别方法的研究历程及主要成果,系统性阐述了典型时、频域方法以及基于函数拟合思想、正则化策略、贝叶斯框架、数据驱动等动载荷识别方法,并讨论了各方法的优缺点以及适用范围。此外,还针对载荷识别过程中普遍存在的结构参数不确定问题以及输入条件不确定问题进行了总结。动载荷位置识别也是动载荷识别问题的重要组成部分,本文对现有位置识别方法进行了归纳分析。探讨了动载荷识别方法的工程应用,并分析了现有方法的局限性。结合当前实际工程应用中日益迫切的需求及动载荷识别领域面临的问题,展望了未来动载荷识别亟需攻克的技术难题以及可能的发展方向和重点领域。

基于在线自组织增量学习的非侵入式负荷识别方法

随着电子技术智能化的发展,对实现电器负荷使用情况的精准识别在智慧用电领域将有广泛的用户需求。为了实现对电器设备的实时在线精确监测,本文提出了一种基于在线自组织增量学习(SOINN)的非侵入式负荷识别方法。该方法包含负荷特征提取、负荷特征分类及电器识别2个步骤。在负荷特征提取步骤中,提出了包含奇次谐波、均值、方差、3阶矩、4阶矩、电流有效值、功率谱峰值、功率谱谷值在内的共12维特征的特征提取方案。在负荷特征分类及电器识别步骤中,提出了结合SVM的SOINN的负荷特征分类及电器识别方法,以克服传统的SOINN算法不能实现电器类型识别功能的缺陷。通过C++语言将所提方法中的功能算法编写成微处理器系统的可执行功能模块,将功能模块移植部署在SoCFPGA的HPS端运行,实现了FPGA和HPS之间的协同高速数据通信。选取了8种常规家用电器作为负荷识别对象,搭建了基于SoCFPGA的硬件实验平台,进行了最优负荷特征选取,并采用本文方法对单电器与多电器的在线负荷进行了识别。实验结果:选取12维特征为本文方法的最优特征组合;本文方法的单电器与多电器的识别率均在95%以上。本文提出的负荷识别方法能够有效、准确地识别单电器与多电器;系统可实施性强,灵活性高,具有在线学习的优越性与实际应用的切实可行性。

基于改进型降噪自动编码器的家用负荷辨识方法

家用负荷辨识准确性受数据采样速率制约显著,过高的采样速率能够解决数据问题,但也带来成本提高、系统设计复杂等问题。基于此,提出了一种仅依赖常规采样速率有功功率量测的非侵入式负荷辨识方法,所提方法对传统的降噪自动编码器算法滑动窗的重叠部分计算进行了改进,使用中值滤波器对重叠窗的数据结果进行处理,能够较好地克服辨识结果偏高的问题。通过在REDD(reference energy disaggregation dataset)和TraceBase两个家庭用电数据集开展测试,证明了所提方法在辨识设备功率和判断设备所处状态两个方面都具有较好的效果,且各项指标均好于经典的基于因子隐马尔可夫模型(factorial hidden Markov model,FHMM)算法。另外所提算法的通用性较好,能够对不同型号、品牌的同种设备进行有效辨识,具有较好的实用价值。

基于贝叶斯正则化的多源/连续冲击载荷识别及试验研究

针对冲击载荷识别中存在病态矩阵求逆的不适定问题和噪声敏感问题,给出一种增广Tikhonov正则化技术的改进贝叶斯方法。通过引入小波阈值方法解决高噪声水平下多源/连续冲击载荷识别精度不佳的问题,在识别过程中自适应地确定最优正则化参数,并有效地剔除噪声对冲击载荷识别的影响。通过开展飞机壁板结构在不同冲击载荷和信噪比噪声下的数值仿真分析,以相关系数和相对误差作为评价指标,对比讨论了基于L曲线法和广义交叉检验法的Tikhonov正则化方法、贝叶斯正则化方法以及该文方法的识别效果,结果表明该文方法兼顾了曲线的光滑性与峰值识别的准确性,在20 dB高噪声水平连续冲击载荷识别时峰值平均误差不超过14%。开展了典型加筋壁板结构的冲击试验,验证了该文方法对实际工程中典型多源/连续冲击载荷的识别能力,峰值平均误差控制在18%以内,为解决工程应用中的载荷识别问题提供了有效途径。

基于广义柔度曲率信息熵的板式轨道脱空损伤识别

板式轨道填充层作为轨道结构关键部位,在高频列车荷载和环境共同作用下出现脱空损伤,引起脱空位置轨道结构刚度改变。为有效检测板式轨道的轨道板脱空情况,采用数值仿真分析得到无砟轨道模态信息,利用轨道脱空区域广义柔度曲率局部峰值进行轨道脱空损伤识别。结合广义柔度、均匀荷载面(Uniform load surface, ULS)、曲率和局部信息熵,提出可定位损伤的ULS曲率信息熵,并在CRTS III板式轨道上进行验证。研究结果表明:广义柔度曲率利用轨道脱空前后模态信息计算轨道脱空损伤曲率差,能够有效定位脱空位置;ULS曲率信息熵表征值只需要轨道的一阶模态信息便能够有效地反映轨道脱空位置及面积,且克服了广义柔度曲率需要健康模态信息的不足;轨道对称位置上相同面积脱空的ULS曲率信息熵值相同;ULS曲率信息熵值与脱空面积和厚度成正相关关系;ULS曲率信息熵表征值具有较好的损伤识别敏感性,能够识别小于单个测点布置面积的0.1 m×0.1 m小面积脱空,并且对轨道板边脱空识别敏感性高于轨道板中脱空识别敏感性。

基于LSTM-CNN的结构固有频率激励下正弦载荷识别方法研究

当外载荷频率达到或接近结构固有频率时,传统载荷识别方法(比如截断奇异值分解法)的识别精度会降低。为此,通过卷积网络的特征提取和长短期记忆网络的长时记忆功能建立LSTM-CNN载荷识别模型,提出一种基于LSTM-CNN模型的载荷识别方法,对GARTEUR飞机模型开展载荷时域波形识别研究。通过采集结构的响应数据和激励数据进行模型训练和载荷识别,并与截断奇异值分解(TSVD)方法、长短期记忆网络(LSTM)方法和深度卷积神经网络(DCNN)方法的识别结果进行对比分析。结果表明:基于LSTM-CNN模型的载荷识别方法可以有效应用于结构固有频率激励下正弦载荷识别问题,具有较高的识别精度和抗噪能力。

基于集群辨识和卷积神经网络-双向长短期记忆-时序模式注意力机制的区域级短期负荷预测

为了解决区域级短期电力负荷预测时输入特征过多和负荷时序性较强的问题,提出一种基于集群辨识和卷积神经网络(convolutional neural networks,CNN)-双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)-时序模式注意力机制(temporal pattern attention,TPA)的预测方法。首先,将用电模式和天气作为影响因素,基于二阶聚类算法对区域内的负荷节点进行集群辨识,再从每个集群中挑选代表特征作为深度学习模型的输入,这样既能减少输入特征维度,降低计算复杂度,又能综合考虑预测区域的整体特征,提升预测精度。然后,针对区域电力负荷时序性的特点,用CNN-BiLSTM-TPA模型完成训练和预测,该模型能提取输入数据的双向信息生成隐状态矩阵,并对隐状态矩阵的重要特征加权,从多时间步上捕获双向时序信息用于预测。最后,在美国加利福尼亚州实例上分析验证了所提方法的有效性。

基于飞行实测载荷的副翼操纵导数识别

某型飞机横向操纵时副翼和扰流板会耦合偏转实现飞机的滚转运动,现有气动力参数辨识方法无法单独识别出副翼和扰流板的操纵导数。基于飞机控制律设计逻辑和滚转运动时结构的受载特点,提出一种基于飞行实测载荷的副翼操纵导数识别方法。通过实测机动飞行中机翼各测载剖面的结构载荷并结合剖面外结构质量与加速度分布,提出了机动飞行中机翼气动载荷的测量方法;结合飞机横向操纵瞬时滚转力矩平衡方程和操纵面偏转逻辑,推导了滚转改出时副翼操纵导数识别过程,提出了基于机翼实测载荷的副翼操纵导数识别方法;基于前述方法开展了副翼操纵导数识别,并分析了不同马赫数对副翼操纵导数的影响。研究表明,基于飞行实测载荷能够进行副翼操纵导数识别,而且同一马赫数下副翼操纵导数识别结果集中度较好,随着马赫数的增加,副翼操纵效能将会降低。飞行载荷实测结果可用于气动参数辨识工作中。