振动控制中传感器优化配置的频域方法及在智能结构中的应用

讨论了振动控制中采用试验模态分析技术实现模态滤波器,以获取模态坐标时传感器的优化配置问题,为了使由频响函数所构成的矩阵条件数量小,以减小模态波器误差,提出了一种次优方法。它逐步消除那些对该矩阵条件数贡献最小的传感器可选位置,一直到只剩下希望的传感器数目的位置为止。过种方法简单易行,效率较高,并通过一个空间智能桁架结构的传感器优化配置研究,说明该方法的有效性。     

Data correlation analysis for optimal sensor placement using a bond energy algorithm

Optimal sensor placement is one of the crucial and fundamental factors for constructing a cost-effective structural health monitoring system and is related to the effective evaluation of the state of the structure. Structural responses are correlated to some extent, as the structural behavior is continuous. Based on the above two considerations, the question arises of how to obtain the maximum amount of information for understanding the structure using measurements from limited sensors and not be limited to direct monitoring at the placements where the limited sensors are located. Data correlation analysis for optimal sensor placement is proposed using a bond energy algorithm, in which the objectives, such as structural response evaluation covering the maximum structural responses using measurements from sensors located at the optimal placements, are taken into account. The data correlation analysis is conducted for the structural responses, and the correlation matrix is established. Furthermore, the optimal sensor placements and the correlation of the responses at element locations can be determined using the bond energy algorithm. A Schwedler single-layer spherical lattice dome-like structure, which is a common large space steel structure, is used to simulate the structural responses and verify the effectiveness of the proposed method by discussion of different scenarios of parameter selection.     

输电铁塔双轴加速度传感器优化布置

研究输电铁塔结构振动测量用双轴加速度传感器的优化布置问题。以三维模态置信准则(TMAC)为基础,提出一种新的考虑减小冗余度的二维模态置信准则法(RRBMAC法),以实现双轴加速度传感器优化布置,该方法基于改进的二维列主元QR分解获得传感器初始布点,再结合BMAC矩阵非对角元最大值和测点冗余度建立目标函数进行寻优。采用EFI2法和RRBMAC法对钢管塔进行传感器优化布置,采用两个水平方向的模态置信准则和最大奇异值比值对布点方案进行评价,结果显示两种方法的优化效果均首先随传感器数量增加而增强,而后趋于平稳,RRBMAC法较EFI2法能够获得更好的布置效果,且在传感器数目较少的情况下,RRBMAC法即表现出较好的优化能力。另外,采用EFI2法和RRBMAC法对一基格构式输电铁塔进行传感器优化布置,对比分析结果显示RRBMAC法能够获得较好的布置方案。采用优化的传感器布置方式对铁塔进行风振响应实测,并采用NEx T-ERA方法识别铁塔固有频率和阻尼系数,与模态分析结果对比显示NEx T-ERA方法能够提取得到较精确的频率结果。     

A Bayesian machine learning method for sensor selection and fusion with application to on-board fault diagnostics

In applications like feature-level sensor fusion, the problem of selecting an optimal number of sensors can lead to reduced maintenance costs and the creation of compact online databases for future use. This problem of sensor selection can be reduced to the problem of selecting an optimal set of groups of features during model selection. This is a more complex problem than the problem of feature selection, which has been recognized as a key aspect of statistical model identification. This work proposes a new algorithm based on the use of a Bayesian framework for the purpose of selecting groups of features during regression and classification. The hierarchical Bayesian formulation introduces grouping for the parameters of a generalized linear model and the model hyper-parameters are estimated using an empirical Bayes procedure. A novel aspect of the algorithm is its ability to simultaneously perform feature selection within groups to reduce over-fitting of the data. Further, the parameters obtained from this algorithm can be used to obtain a rank order among the selected sensors. The performance of the algorithm is first tested on a synthetic regression example. Finally, it is applied to the problem of fault detection in diesel engines (30,000 data records from 43 sensors, 8 classes) and used to compare the misclassification rates with a varying number of sensors.     

Optimal placement of sensors and actuators using measures of modal controllability and observability in a balanced coordinate

In this paper, a method for optimal placement of sensors and actuators is presented by using new measures of modal controllability and observability defined in a balanced coordinate system. The proposed new measures are shown to have a great advantage in practical use when they are used as criteria for selecting the locations of sensors and actuators, since the most controllable and observable locations can be obtained to be identical. In addition, they are more accurate than the measures of Hamdan and Nayfeh in that the effects of the eigenvector norm are considered into the magnitude of measures. In simulations, to verify the effectiveness of the proposed measures and optimal placement method, the closed-loop response of a simply supported flexible beam, in which the number and locations of actuators are determined by using the proposed measures and optimal placement method, has been examined and compared with the case of Hamdan and Nayfeh’s measures.     

新型机器人6维力／力矩传感器结构的刚度性能指标分析

提出了一种新颖的6维力/力矩传感器并联结构,应用并联机器人机构学理论求出其刚度矩阵,定义其刚度性能指标,并对其刚度性能指标进行分析计算,为该传感器的设计与应用提供理论依据。分析计算结果表明,该并联结构的6维力/力矩传感器是位移刚度和扭转刚度各项同性的,可应用到机器人手腕、手指和其它使用多维力/力矩传感器的场合。     

拱结构模态测试中传感器优化配置

将有效独立法(EFI)和有效独立-驱动点残值法(EFI-DPR)应用到拱结构的模态分析传感器优化布置中,通过与传统的均分法(ED)进行数值和试验比较,找出传感器优化布置的有效方法和合理方案.首先利用Ansys有限元软件进行拱结构模态分析的数值模拟,分别用3种方法对其进行传感器优化布置;然后分别用均方差最小准则和模态保证准则对其优化结果进行验证和比较,找出最适合的优化方法;最后结合拱结构模态试验,提出了传感器优化布置方案.结果表明,在拱结构模态试验中,ED法具有一定的合理性,但其随机性较大.EFI法克服了这一缺点,且随着传感器数目的改变,其均方差呈规律性变化,为确定传感器优化数目提供了依据,其优化效果优于其他两种方法.     

基于属性层次模型的单工位状态监测异类传感器布置优化

为了获得足够有效的切削过程状态信息,确保产品质量/系统零部件的安全可靠运行,应采用可靠的监测策略并对传感器进行优化布置。针对单工位多工步切削过程状态监测,基于多工位误差流(Stream of variation,SOV)理论构建单工位多工步信息流模型,通过状态空间变换和主成分分析确定故障/监测量信息传递系数用来表征不同测点传感器的监测能力;考虑传感器以及故障/监测量之间的特性差异对于系统检测能力的影响,采用6Sigma的因果矩阵(Cause-effect matrix, CEM)和失效模式与影响分析(Failure mode and effect analysis, FMEA)工具分别对传感器以及故障/监测量特性进行量化表示;基于属性层次模型(Attribute hierarchical model, AHM)构建传感器,故障/检测量以及系统检测能力之间的因果关系,设定优化目标和约束条件,并采用元启发式算法-混合蛙跳算法(Shuffled frog leaping algorithm, SFLA)和遗传算法(Genetic algorithm, GA)用于优化计算。提出基于单工位状态监测的六步传感器优化布置策略。实例分析表明,在一定约束条件下,就优化目标而言, SFLA显示比GA更高的优化效率,为单工位状态监测的传感器布置优化提供实践参考。     

Deploying autonomous sonobuoys optimally on a linear array via assignment problem

A sonobuoy is an underwater acoustic sensor primarily deployed by airdrop. An acoustic sensor plays a significant role in conducting anti-submarine warfare for detecting underwater sounds. As a future underwater acoustic sensor, sonobuoy is expected to have an ability of self-movement, i.e., autonomous sonobuoy. In this study, we consider an optimal linear array deployment of autonomous sonobuoys when detecting an unknown underwater sound, assuming that the initial locations of sonobuoys are random in a specified operational area. Deploying sonobuoys from the initial locations onto a specific line requires to first determine the line and positions on that line. Under the interval equivalence between two adjacent sonobuoys, we propose two lines, one that is perpendicular from the underwater sound source location and the other based on the linear regression analysis using the initial locations of sonobuoys. With these two lines onto which sonobuoys will be deployed, we provide two optimization models, total distance minimization and maximum distance minimization, which turn out to be assignment and bottleneck assignment problems, respectively, with nonlinear cost coefficients in the objectives. Due to non-convexity of the objectives, we solve these problems for a fixed interval repetitively as a linear program and integer program, respectively. Computational results show that regression-based line provides higher solution qualities for both problems. Finally, we suggest an approximate interval in a heuristic manner, which guarantees a highly near-optimality as the number of sonobuoys increases. Our results may support a decision-making and provide practitioners with insight on more variety of effective use of autonomous sonobuoys.     

基于深度学习的传感器优化布置方法

针对传感器优化布置(optimal sensor placement,简称OSP)问题,提出了一种新的使用深度神经网络的解决方案,并以简化的桥梁形状的桁架结构中的振动测试传感器优化为例进行了验证。首先,选择一种传统的传感器优化布置方法,对自动化生成的大量不同的桁架结构分别进行传感器优化布置计算,将所得优化布置结果在进行数据预处理后构建出深度学习方法所需要的训练集与验证集;其次,使用Python语言和深度学习框架TensorFlow设计实现与本研究问题适配的深度神经网络模型并训练;然后,随机生成了新的桁架结构参数;最后,将深度神经网络输出的传感器布置结果和传统方法的计算结果进行了比较,验证了本研究方法的有效性以及在速度上、可移植性与可扩展性方面的性能优势。     

基于改进粒子群算法的应变传感器优化布置

在结构健康监测和损伤识别研究中,为了应用有限的试验设备资源获取尽可能多的有效测试信息,快速有效地解决应变传感器的优化配置问题,提出了一种基于克隆选择和离散粒子群混合算法优化新型适应度函数的应变传感器优化布置方法,并将该方法应用到拉西瓦拱坝上。结果表明,基于改进克隆选择和离散粒子群混合算法具有更强的全局寻优能力,且提出的应变类适应度函数在保证应变模态正交性和模态应变能方面更有优势。该方法能很好地识别拱坝的应变振型,可在各类结构的模态测试和损伤识别研究中进行推广。     

应变传感器优化配置研究

传统传感器优化配置准则是以位移模态为目标对传感器的位置进行优化配置，不适用于应变传感器位置的优化。为此，本文针对应变传感器优化配置问题，理论推导出应变模态具有正交性，并以此为基础提出基于应变模态保证准则的虚变传感器优化配置准则，对利用应变模态矩阵信息阵初步拟定出的一小组传感器位置进行了研究。最后以一个复合材料四端简支板为例，对本文方法的可行性进行分析研究，结果表明，本文所建议的方法能更加有效地配置虚变传感器。     

基于SSA-ELM的双层十字梁结构光纤布拉格光栅传感器三维力解耦

针对三维力传感器维间耦合干扰严重的问题,以双层十字梁结构光纤布拉格光栅三维力传感器为研究对象,提出了基于麻雀搜索算法优化极限学习机(Sparrow Search Algorithm–Extreme Learning Machine,SSA-ELM)的解耦算法.首先,研究了光纤布拉格光栅的传感及测力原理,揭示该三维力传感...     

Influence of time and length size feature selections for human activity sequences recognition

In this paper, Viterbi algorithm based on a hidden Markov model is applied to recognize activity sequences from observed sensors events. Alternative features selections of time feature values of sensors events and activity length size feature values are tested, respectively, and then the results of activity sequences recognition performances of Viterbi algorithm are evaluated. The results show that the selection of larger time feature values of sensor events and/or smaller activity length size feature values will generate relatively better results on the activity sequences recognition performances.     

基于能量衰减比的双通道源数目估计方法

利用源信号到达两传感器间具有不同能量衰减比的特性,提出了一种新的源数估计方法,解决了因传感器数量不足而无法准确估计源信号数目的问题。首先,利用线性时频变换方法得到两观测信号在频域的能量分布,然后,计算能量散点图中对应角度上的能量总和;最后,通过峰值检测法实现源数目的自动检测。通过理论分析、仿真和实验,证明了该方法的有效性。本方法为盲源分离算法处理振动、噪声信号,提供了可靠的先验信息。     

面向桥梁状态评估的传感器优化配置

以模态置信度矩阵(modal assurance criterion简称MAC)、Fisher信息阵和振型误差为优化准则,采用整数编码遗传算法,探讨了模态挠度法应用中的有限元模型修正和模态挠度计算的传感器优化配置问题。与常用的平分法、有效独立法及基于QR分解的逐步累积法比较,优化配置结果表明,整数编码遗传算法优于上述3种方法。     

A path generation algorithm of an Automatic Guided Vehicle using sensor scanning method

In this paper, a path generation algorithm that uses sensor scannings is described. A scanning algorithm for recognizing the ambient environment of the Automatic Guided Vehicle (AGV) that uses the information from the sensor platform is proposed. An algorithm for computing the real path and obstacle length is developed by using a scanning method that controls rotating of the sensors on the platform. The AGV can recognize the given path by adopting this algorithm. As the AGV with two-wheel drive constitute a nonholonomic system, a linearized kinematic model is applied to the AGV motor control, An optimal controller is designed for tracking the reference path which is generated by recognizing the path pattern. Based on experimental results, the proposed algorithm that uses scanning with a sensor platform employing only a small number of sensors and a low cost controller for the AGV is shown to be adequate for path generation.     

A Bayesian approach to optimal sensor placement for structural health monitoring with application to active sensing

This paper introduces a novel approach for optimal sensor and/or actuator placement for structural health monitoring (SHM) applications. Starting from a general formulation of Bayes risk, we derive a global optimality criterion within a detection theory framework. The optimal configuration is then established as the one that minimizes the expected total presence of either type I or type II error during the damage detection process. While the approach is suitable for many sensing/actuation SHM processes, we focus on the example of active sensing using guided ultrasonic waves by implementing an appropriate statistical model of the wave propagation and feature extraction process. This example implements both pulse-echo and pitch-catch actuation schemes and takes into account line-of-site visibility and non-uniform damage probabilities over the monitored structure. The optimization space is searched using a genetic algorithm with a time-varying mutation rate. We provide three actuator/sensor placement test problems and discuss the optimal solutions generated by the algorithm.     

Frequency response based sensor placement for the mid-frequency range

Accurately characterizing mid-frequency vibrations is essential for structures that require ultra-quiet vibration environments. Selecting the proper sensor locations is an important step in the model verification and validation process. State-of-the-practice approaches to sensor placement are typically based on modes shapes of the pretest finite element model. However, these modal based techniques break down in the mid-frequency range due to the high modal density. The purpose of this work was to develop a sensor placement technique based directly on a structure's frequency response. The finite element model frequency response can be decomposed into principal directions and their corresponding singular values, which relate the principal directions to the system's energy. A system's response is usually dominated by a relatively small number of principal directions, even for frequency bands with high modal densities. Principal directions are always orthogonal, while mode shapes in general are not, which makes them more robust to modeling errors and experimental noise. The new method places sensors such that the independence and signal strength of the dominant principal directions are maintained.     

可控度/可观度在智能结构热压电致动器/传感器配置中的应用

研究智能结构中热压电致动器/传感器配置的优化问题。提出了热压电介质的动态控制方程。提出目标函数来评价系统可控度、可观度以优化致动器/传感器的放置,用具体算例讨论了热压电致动器/传感器的位置和数目对系统可控度、可观度的影响,由此得到了有关致动器/传感器配置的结论。