REAL-TIME MODAL PARAMETER ESTIMATION USING SUBSPACE METHODS: APPLICATIONS

This article describes the underlying theory and hardware implementation of a newly developed algorithm for online modal parameter identification. An online modal parameter estimation algorithm using subspace methods is applied to both model and experimental data for a 4-m laboratory truss structure. Experimental evaluation of this algorithm demonstrates that the technique accomplishes the objective of tracking multiple modes of a complex dynamical system using multiple sensors. The time-varying behaviour is captured in real time via a graphical display of the frequencies and the damping ratios of the system. It is shown that the recursive algorithm provides results similar to the batch algorithm for a time-invariant system. In addition, it is shown that the batch algorithm used to derive the recursive algorithm performs similarly to a newly derived batch algorithm that is closely related to the Eigensystem Realization Algorithm. Details concerning the digital signal processor implementation and off-line monitoring are also presented.     

Recursive subspace identification for on-line tracking of structural modal parameter

The objective of this paper is to develop an on-line tracking of system parameter estimation and damage detection techniques using response measurements. To avoid the singular-value-decomposition in data Hankel matrix, a new subspace identification algorithm was developed. Seismic response data of a 3-story steel frame with abrupt change of inter-story stiffness from the shaking table test was used to verify the proposed recursive subspace identification (RSI) method by using both input and output measurements. With the implementation of forgetting factor in RSI method the ability of on-line damage detection of the abrupt change of structural stiffness can be enhanced. Then, the recursive stochastic subspace identification (RSSI) algorithm is also developed for continuous structural health monitor of structure by using the output-only measurements. Verification of the proposed RSSI method by using the white noise response data of a 2-story reinforced concrete frame from its low level white noise excitation was used. Discussion of the subspace identification model parameters is also investigated.     

一种新的固有频率跟踪及虚假频率剔除方法

针对时变参数辨识中常见的固有频率辨识和虚假频率剔除问题,引入了一种基于新信息准则的子空间跟踪辨识算法,结合实验提出了一种消除虚假固有频率的后处理方法。首先,利用基于新信息准则的子空间跟踪算法辨识出伪时变模态参数;其次,通过聚类方法估计各阶伪固有频率;最后,利用滑动的数据窗比对数据剔除虚假频率。该方法仅需要给出预估的活动模态数即可。不同频率变化形式的仿真算例结果证明了本研究方法较其他辨识算法在信号信噪比较低时具有较高的辨识精度。高温环境下的时变模态实验也验证了该方法的可行性,说明该方法在实际工程中具有较高的应用价值。     

Grey forecasting run-to-run control system in copper chemical mechanical polishing

In this paper, an online grey forecasting run-to-run control system was proposed with the integration of run-to-run control system, recursive least-squares (RLS) algorithm, and grey forecasting model (GFM). One of the objectives of this study is to explore the possibility and feasibility of applying GFM to run-to-run control system in copper chemical mechanical polishing. Under the condition of limited experiment data, GFM is excellent at estimating and forecasting error of the next batch online. To keep the process under control, the controllers are then employed to adjust the process parameters in order to compensate the error. In addition, the RLS algorithm is used to construct dynamically a system estimation matrix for the purpose of stating precisely the relationship between process quality and process parameters, and to consequently improve processing performances. From the computer simulation and the experiment results, the proposed new method developed in this study was able not only to confine the processing performances’ error within the range of 5% but also to supplement, when the process parameters are saturated, the control capability through adjusting other unsaturated process parameters, thus maintaining good processing performances.     

基于过采样投影近似基追踪的无人机异常检测

针对无人机飞行模式切换导致飞行数据在线异常检测准确率低的问题,提出基于过采样投影近似基追踪(OSPABP)的在线异常检测框架。首先利用滑窗和Z-score变换消除飞行数据流量纲,并抽取相关的飞行数据子集;然后过采样当前时刻子集的输入数据,放大异常数据对数据子空间的影响,并通过在线估计和追踪匹配过采样后数据子空间的投影近似基方向变化,从而判断子集实时输入数据的异常程度。同时该方法还可抑制飞行模式切换对异常检测效果的影响。采用Flight Gear模拟飞行数据和明尼苏达大学真实无人机飞行数据的实验结果表明,所提出方法对飞行模式切换敏感度低,可明显降低异常检测的误检率,并有效提高检测准确率。此外,算法的计算和存储复杂度均可满足机载处理要求。     

应用人工蜂群算法的动态波达方向跟踪

针对目标信号源波达方向(DOA)的实时变化,将人工蜂群算法应用于最大似然函数的优化,实现了动态目标DOA的实时跟踪。首先,提出了一种可变遗忘因子的自适应样本协方差矩阵更新方法,该方法可根据目标信号源DOA变化的快慢自适应调整历史数据和当前采样数据在协方差矩阵中所占的权重,从而保证在获得较小稳态误差的同时又可获得较快的跟踪速度。然后,直接应用了性能优越的最大似然估计方法,避免了子空间跟踪类算法需要不断重复特征值或奇异值分解等问题。最后,采用人工蜂群仿生智能算法对似然函数的求解进行优化,从而极大地减少了算法的计算量,保证了算法的快速性和实时性。实验结果表明:在单快拍采样的情况下,信噪比为0dB时,跟踪两个目标信号源的均方根误差为0.995 2°,基本达到了阵列信号处理中目标跟踪方法的设计要求。     

基于稀疏正则约束的子空间视觉跟踪

针对子空间表示跟踪算法处理遮挡问题的能力不足,以及稀疏表示跟踪算法无法满足跟踪实时性要求等问题,本文提出一种稀疏正则约束的子空间视觉跟踪算法。该算法结合了子空间表示与稀疏表示的优势,提升了对于遮挡问题的处理能力,并且降低了算法的计算复杂度。首先,该算法利用PCA子空间基向量集、子空间均值以及表示残差对目标进行表示,同时算法采用L2范数作为表示系数以及表示残差的稀疏约束函数。其次,算法采用了一种分步循环迭代的方法求解表示模型的系数与残差。然后,为了保证子空间基向量与空间均值能够持续准确的描述目标在跟踪过程中出现的变化,算法根据经过开运算处理后的表示残差中非零元素的不同比率构建不同的更新模板,并结合增量主成分分析方法在线学习新的基向量与均值。最后,在实验部分,本文将提出算法在10个实验序列上的跟踪结果与8个现今主流跟踪算法进行对比,同时从定性与定量两个方面对实验结果进行分析。本文算法在全部10个实验序列上的平均中心误差为5.3pixel,平均覆盖率为77%,相比于其他算法,本文算法取得了较高的跟踪精度。本文算法具有更好的鲁棒性,并且满足更多场景下的跟踪需求。     

复杂交通流下基于卡尔曼滤波的多目标全生命周期状态估计

针对复杂行车环境下噪声干扰和车辆行车过程中状态变化导致交通场景中目标状态估计精度低的问题,以毫米波雷达 为检测传感器,提出涵盖参数初始化和在线更新的基于卡尔曼滤波的多目标全生命周期状态估计方法。 首先,建立交通流下多 目标运动状态的卡尔曼滤波状态估计模型;基于此,一方面提出基于数据驱动的卡尔曼滤波观测噪声协方差矩阵初始化的新方 法,另一方面采用变分贝叶斯方法对卡尔曼滤波参数进行在线更新,以此提高多目标状态估计精度;最后,在算法实现步骤的基 础上,利用实车数据开展测试验证工作。 实验结果表明,方法的目标状态估计均方误差为 0. 153,相较于传统卡尔曼滤波减小 了 36. 2% ,证明所提出方法对提升车辆感知精度的有效性。     

基于环境激励的厂房结构损伤诊断与安全评价

以"5.12"汶川地震震中某地下厂房上部结构为研究对象,进行基于环境激励的厂房结构损伤诊断与安全评价研究。根据现场实测的结构动位移时程,分别用随机子空间方法和系统特征实现方法对厂房结构的模特参数进行时域辨识,依据结构特定部位损伤会引起结构模态参数变化的原则,提出运用距离的概念对结构所属的健康状况进行量化评价的方法。研究结果表明,厂房上部结构基本完整,与现场取芯结果一致。研究成果对于类似大型水工结构的安全检测与评价具有一定的参考价值。     

生产时间可变间歇过程的Petri网模型及其调度

讨论了生产时间可变的多产品间歇过程的最优调度问题,给出了间歇过程在复杂中间的无限存储策略、有限存储策略、无中间存储策略和混杂存储策略下p-时间Petri网模型的描述方法,进而给出了基于可行调度集和修正分枝界定的间歇过程最短生产时间的最优调度算法.该算法利用一间歇过程最短生产时间不大于另一间歇过程最短生产时间的条件,有效地限制了对解空间的搜索,进而改善了算法的计算性能.仿真算例表明了所述方法的有效性.     

基于递推最小二乘法的地磁测量误差校正方法

针对弹体地磁测量容易受到各种误差影响而导致地磁姿态测量精度降低的问题,在分析自身误差和环境误差的基础上,对椭球模型的地磁测量误差进行建模,采用最大似然估计解算静态误差补偿参数,以解算结果为初值,通过递推最小二乘法推到补偿参数的实时更新算法,综合以上研究,形成用于地磁测量误差补偿的在线组合校正方法。仿真及实验结果表明,在接近盲区方向的最大姿态角误差小于5°,在线组合校正能够保证姿态检测系统在不同射向条件下的精度。     

Double-layer ensemble monitoring of non-gaussian processes using modified independent component analysis

The modified independent component analysis (MICA) was proposed mainly to obtain a consistent solution that cannot be ensured in the original ICA algorithm and has been widely investigated in multivariate statistical process monitoring (MSPM). Within the MICA-based non-Gaussian process monitoring circle, there are two main problems, i.e., the selection of a proper non-quadratic function for measuring non-Gaussianity and the determination of dominant ICs for constructing latent subspace, have not been well attempted so far. Given that the MICA method as well as other MSPM approaches are usually implemented in an unsupervised manner, the two problems are always solved by some empirical criteria without respect to enhancing fault detectability. The current work aims to address the challenging issues involved in the MICA-based approach and propose a double-layer ensemble monitoring method based on MICA (abbreviated as DEMICA) for non-Gaussian processes. Instead of proposing an approach for selecting a proper non-quadratic function and determining the dominant ICs, the DEMICA method combines all possible base MICA models developed with different non-quadratic functions and different sets of dominant ICs into an ensemble, and a double-layer Bayesian inference is formulated as a decision fusion method to form a unique monitoring index for online fault detection. The effectiveness of the proposed approach is then validated on two systems, and the achieved results clearly demonstrate its superior proficiency.     

Discriminative sparse subspace learning and its application to unsupervised feature selection

In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters׳ assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches.     

An improved stochastic subspace identification for operational modal analysis

An improved stochastic subspace identification algorithm is introduced to solve the low computational efficiency problem of the Data-driven stochastic subspace identification. Compared with the conventional algorithm, it needs much less cost of memory and computing time because it does not have a process of the QR decomposition of Hankel matrix. Model similarity index is proposed to measure the reliability of the modes obtained by the improved stochastic subspace identification. Furthermore, the stabilization diagram in combination with the modal similarity index is adopted to effectively indicate spurious modes resulting from noise and model redundancy. A criterion named the modal norm is introduced to indicate the dominating mode. A numerical example on the parameter estimation of a linear time-invariant system of 7 degrees of freedom and one experimental example on the parameter estimation of Chaotianmen bridge model in Chongqing are presented to demonstrate the efficacy of the method.     

基于 DPC 指纹子空间匹配的室内 WiFi 定位方法

针对无线接收信号强度 (RSS) 受传播环境突发噪声影响从而引起指纹定位较大误差的问题,本文提出了一种指纹子 空间匹配结合密度峰值聚类 (DPC) 的定位算法,有效避免大误差点。 首先通过在线阶段目标 RSS 信号的接入点 (AP) 覆盖 向量,确定有效的参考位置点,并划分多个指纹子空间,利用改进的 WKNN 算法估计目标在每个子空间内的位置;最后利用 DPC 算法选取决策值最大的 S 个估计位置确定目标。 所提算法简单,不需要离线阶段的学习过程训练定位模型,尤其适合存在 大量 AP 的大范围室内定位区域。 实际环境中的定位实验表明,基于 DPC 的指纹子空间匹配算法比 WKNN 算法的定位精度提 升了 25% 左右,且在参考点分布密度为 1. 8 m × 1. 8 m 的实验条件下基本消除了 4 m 以上的大定位误差,有效提高了定位方法 的整体性能。     

在线压缩感知方法及其在漏磁检测中的应用

以长距离油管的漏磁检测系统为研究对象,研究了漏磁检测数据的在线压缩算法。针对嵌入式在线工作环境下,传统的数据压缩方法难以应用的问题,引入压缩感知(CS)理论,提出了漏磁检测数据在线CS压缩方法。确定了小波基作为漏磁信号的最佳稀疏表示基,并推导了小波稀疏基矩阵的数学表达公式;提出Welch界和PRP共轭梯度算法的测量矩阵优化算法;提出了漏磁检测数据的重要数据段筛选方法,极大地减少了数据存储量。仿真试验证明了所提出在线压缩算法极大地减少了在线环境压缩编码的运算复杂度,具有简单迅速、压缩比高、重构精度高等优点,符合漏磁检测数据在线压缩的实际要求。     

Finite time parameter estimation-based adaptive predefined performance control for servo mechanisms

This paper proposes an adaptive funnel control scheme for two-inertia servo systems with unknown parameters (e.g., inertia and stiffness coefficient). To improve the transient and steady-state performance, a modified funnel variable, which relaxes the limitation of the original funnel control, is developed by using the tracking error to replace the scaling factor. Then, an error transformation with the new funnel variable is introduced and used in the controller design. An auxiliary filter operation is designed to derive the information of parameter estimation error, which is used as a new leakage term in the parameter update law. Then a sliding mode technique is introduced in the adaption law to achieve finite-time convergence. Appropriate comparison to the gradient descent method is provided concerning with the estimation convergence. Simulation and experimental results are used to illustrate the effectiveness of the devised control scheme.     

A hybrid EDA with ACS for solving permutation flow shop scheduling

This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard’s benchmark data sets demonstrate that the proposed algorithm generated high-quality solutions by comparing with the existing population-based search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.     

Online automatic identification of the modal parameters of a long span arch bridge

The “Infante D. Henrique” bridge is a concrete arch bridge, with a span of 280 m that crosses the Douro River, linking the cities of Porto and Gaia located in the North of Portugal. This structure is being monitored by a recently installed dynamic monitoring system that comprises 12 acceleration channels. This paper describes the bridge structure, its dynamic parameters identified with a previously developed ambient vibration test, the installed monitoring equipment and the software that continuously processes the data received from the bridge through an Internet connection. Special emphasis is given to the algorithms that have been developed and implemented to perform the online automatic identification of the structure modal parameters from its measured responses during normal operation. The proposed methodology uses the covariance driven stochastic subspace identification method (SSI-COV), which is then complemented by a new algorithm developed for the automatic analysis of stabilization diagrams. This new tool, based on a hierarchical clustering algorithm, proved to be very efficient on the identification of the bridge first 12 modes. The results achieved during 2 months of observation, which involved the analysis of more than 2500 datasets, are presented in detail. It is demonstrated that with the combination of high-quality equipment and powerful identification algorithms, it is possible to estimate, in an automatic manner, accurate modal parameters for several modes. These can then be used as inputs for damage detection algorithms.     

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies.