PARAMETER IDENTIFICATION IN OFFSHORE PLATFORM USING ARMA MODEL AND TECHNOLOGY OF EXTRACTING FREE VIBRATION SIGNAL

A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi_values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale_model offshore platform was used to evaluate the technology of efficiency and the value of on_line.

Zonotope parameter identification for piecewise affine systems

The problem of how to identify the piecewise affine system is studied in this paper, where this considered piecewise affine system is a special nonlinear system. The reason why it is not easy to identify this piecewise affine system is that each separated region and each unknown parameter vector are all needed to be determined simultaneously. Then, firstly, in order to achieve the identification goal, a multi-class classification process is proposed to determine each separated region. As the proposed multi-class classification process is the same with the classical data clustering strategy, the multi-class classification process can combine the first order algorithm of convex optimization, while achieving the goal of the classification process. Secondly, a zonotope parameter identification algorithm is used to construct a set, which contains the unknown parameter vector. In this zonotope parameter identification algorithm, the strict probabilistic description about the external noise is relaxed, and each unknown parameter vector is also identified. Furthermore, this constructed set is consistent with the measured output and the given bound corresponding to the noise. Thirdly, a sufficient condition about guaranteeing our derived zonotope not growing unbounded with iterations is formulated as an explicit linear matrix inequality. Finally, the effectiveness of this zonotope parameter identification algorithm is proven through a simulation example.

DPCM-based vibration sensor data compression and its effect on structural system identification

Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.

Automatic modal parameter identification of high arch dams:feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(DBSCAN) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

Dynamic Characteristics Analysis and Vibration Experiment of Upper-time of Flight Counter (U-ToFC)

The dynamic characteristic parameters of Up-time of Flight Counter （U-ToFC） are important for research of structure optimization and reliability. However, the current simulation is performed based on homogenous material and simplified constraint model, the correct and reliability of results are difficult to be guaranteed. The finite element method based on identification of material parameters is proposed for this investigation on dynamic analysis, simulation and vibration experiment of the U-ToFC. The structure of the U-ToFC is complicated. Its＇ outside is made of aluminum alloy and inside contains electronic components such as capacitors, resistors, inductors, and integrated circuits. The accurate material parameters of model are identified difficultly. Hence, the parameters identification tests are performed to obtain the material parameters of this structure. On the basis of the above parameters, the experiment and FEA are conducted to the U-ToFC. In terms of the flight acceptance test level, and two kinds of joints condition between the U-ToFC and fixture are considered. The natural frequencies, vibration shapes and the response of the power spectral density of the U-ToFC are obtained. The results show simulation which is based on parameters identification is similar with vibration experiment in natural frequencies and responses. The errors are less than 10%. The vibration modes of simulation and experiment are consistent. The paper provides a more reliable computing method for the dynamic characteristic analysis of large complicated structure.

A novel none once per revolution blade tip timing based blade vibration parameters identification method

The vibration caused blade High Cycle Fatigue(HCF)is seriously affects the safety operation of turbomachinery especially for aero-engine.Thus,it is crucial important to identify the blade vibration parameters and then evaluate the dynamic stress amplitude.Blade Tip Timing(BTT)method is one of the promising method to solve these problems.While,it need a high resolution Once Per Revolution(OPR)signal which is difficult to get for the aero-engine.Here,a Coupled Vibration Analysis(CVA)method for identifying blade vibration parameters by a none OPR BTT is proposed.The method assumes that every real blade has its own vibration performance at a given speed.Whereby,it can take any blade as the reference blade,and the other blades using the reference blade as the OPR for vibration displacement calculating and further parameter identifying.The proposed method is validated by numerical model.Also,experimental studies are carried out on a straight blade and a twisted three dimensional blade test rig as well as a large industrial axial compressor respectively.The results show that the proposed method can accurately identify the blade synchronous vibration parameters and quantitatively evaluate the mistuning in bladed disks,which lays a foundation for the reliability improvement of aero-engine.

An inertial parameter identification method of eliminating system damping effect for a six-degree-of-freedom parallel manipulator

A new simple and effective inertial parameter identification method based on sinusoidal vibrations of a six-degree-of-freedom parallel manipulator is proposed. Compared with previously known identification algorithms, the advantages of the new approach are there is no need to design the excitation trajectory to consider the condition number of the observation matrix and the inertial matrix can be accurately defined regardless of the effect of viscous friction. In addition, the use of a sinusoidal exciting trajectory allows calculation of the velocities and accelerations from the measured position response. Simulations show that the new approach has acceptable tolerance of dry friction when using a simple coupling parameter modified formula. The experimental application to the hydraulically driven Stewart platform demonstrates the capability and efficiency of the proposed identification method.

IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequencyrepresentation and Hubert transform is proposed to identity modal parameters of linear time-varyingsystems from measured vibration responses. Using Gabor expansion and synthesis theory, measuredresponses are represented in the time-frequency domain and modal components are reconstructed bytime-frequency filtering. The Hilbert transform is applied to obtain time histories of the amplitudeand phase angle of each modal component, from which time-varying frequencies and damping ratios areidentified. The proposed method has been demonstrated with a numerical example in which a lineartime-varying system of two degrees of freedom is used to validate the identification scheme based ontime-frequency representation. Simulation results have indicated that time-frequency representationpresents an effective tool for modal parameter identification of time-varying systems.

Bayesian system identification and chaotic prediction from data for stochastic Mathieu-van der Pol-Duffing energy harvester

In this paper,the approximate Bayesian computation combines the particle swarm optimization and se-quential Monte Carlo methods,which identify the parameters of the Mathieu-van der Pol-Duffing chaotic energy harvester system.Then the proposed method is applied to estimate the coefficients of the chaotic model and the response output paths of the identified coefficients compared with the observed,which verifies the effectiveness of the proposed method.Finally,a partial response sample of the regular and chaotic responses,determined by the maximum Lyapunov exponent,is applied to detect whether chaotic motion occurs in them by a 0-1 test.This paper can provide a reference for data-based parameter iden-tification and chaotic prediction of chaotic vibration energy harvester systems.

Identification of Modal Parameters with Linear Structure under Non-stationary Ambient Excitation

Empirical mode decomposition (EMD) is proposed to identify linear structure under non-stationary excitation,and non-white noise coefficient is introduced under the assumption of random signals consisting of white noise and non-white noise signals. The cross-correlation function of response signal is decomposed into mode functions and residue by EMD method. The identification technique of the modal parameters of single freedom degree is applied to each mode function to obtain natural frequencies, damping ratios and mode shapes. The results of identification of the five-degree freedom linear system demonstrate that the proposed method is effective in identifying the parameters of linear structures under non-stationary ambient excitation.

THE ACCOMPANIED SLOWLY-VARIANT-SYSTEM OF NONLINEAR DYNAMIC SYSTEMS

The slowly-variant-system is defined and analyzed in this paper and the nonlinear relationship between its instantaneous parameters and the instantaneous amplitude and frequency of its free vibration response is established. By defining the band-pass mapping, a slowly-variant-system which we call the accompanied slowly-variant-system is extracted from the nonlinear system; and the relationship between the two systems is discussed. Also, the skeleton curves that can illustrate the nonlinearity and the main properties of the nonlinear system directly and concisely are defined. Work done in this paper opens a new way for nonlinearity detection and identification for nonlinear systems.

PROPER APPLICATION OF A KIND OF MATRIX CON-STRUCTION METHOD IN PHYSICAL PARAMETER IDENTIFICATION OF DYNAMIC MODEL

The expressions of matrix construction by using the singular value decomposition (SVD) are applied to the physics parameter identification of dynamic model. Then, based upon to the characteristics of a kind of matrix construction method, the orders of the parameter identification model can be reduced. After reducing, the mathematics and physics correspondence relations between the subsystem and the original system are distinct. the condensation errors can be avoided. The numerical example shows the benefit of the presented methodology.

Identification of LPV system using locally weighted technique

The problem of linear parameter varying （LPV） system identification is considered based on the locally weighted technique which provides estimation of the LPV model parameters at each distinct data time point by giving large weights to measurements that are ＂close＂ to the current time point and small weights to measurements ＂far＂ from the current time point. Issues such as choice of distance function, weighting function and bandwidth selection are discussed. The developed method is easy to implement and simulation results illustrate its efficiency.

Adaptation in Stochastic Dynamic Systems—Survey and New Results IV: Seeking Minimum of API in Parameters of Data

This paper investigates the problem of seeking minimum of API (Auxiliary Performance Index) in parameters of Data Model instead of parameters of Adaptive Filter in order to avoid the phenomenon of over parameterization. This problem was stated by Semushin in [2]. The solution to the problem can be considered as the development of API approach to parameter identification in stochastic dynamic systems.

采用激励轨迹实现机器人关节伺服动刚度的辨识

针对传统伺服动刚度辨识方法操作繁琐且阶跃力矩加载方式存在安全隐患等缺点,提出一种采用激励轨迹实现工业机器人关节伺服动刚度辨识的方法。该方法以五阶傅里叶级数轨迹为激励轨迹输入,通过使用最小二乘法对采集的关节位置和关节电机力矩等数据拟合辨识获得工业机器人关节伺服动刚度和动力学模型参数,并以其计算关节伺服扰动力矩。采用提出方法与传统辨识法所获伺服动刚度来进行扰动力矩补偿,并依据补偿后轨迹位置跟踪误差来评判两种辨识方法的优劣。在工业机器人平台上分别对单关节运动和六个关节运动的扰动力矩补偿实验,结果表明提出的激励轨迹辨识方法获得伺服动刚度的精度优于传统辨识方法所得伺服动刚度的精度。

协方差驱动随机子空间辨识的参数敏感性分析

协方差驱动随机子空间辨识(Covariance-driven Stochastic Subspace Identification,SSI-cov)是近年来发展较为成熟的工作模态分析方法。针对其识别精度和效率对参数设置具有较高敏感性的问题,基于敏感性分析方法,利用奇异熵增量跳跃点明显程度、频率平均识别误差、阻尼比总变异系数、振型平均模态置信因子、运行时间5种评价指标以及稳定图,通过一经典五自由度层模型仿真算例,研究Toeplitz矩阵行块数、采样频率、数据长度对SSI-cov识别结果的影响规律。并给出既能满足精度要求又可控制程序运行时间的参数建议取值范围。最后,通过一缩尺三层框架模型在白噪声激励下实测数据对提出的根据SSI-cov改进参数设置进行验证,结果表明提出的各参数建议取值范围均较为合理。

基于环境振动试验的系杆拱桥模态参数识别

对桥梁开展环境振动测试,可以获得桥梁的模态参数如固有振型、自振频率以及阻尼比。桥梁性能评定、损伤识别、健康检测和基准有限元模型建立以这些参数为主要依据。将实测的加速度数据进行模态分析,获得桥梁的自振频率和振型,并与有限元模型理论计算结果进行对比分析,结果表明:通过试验得到的桥梁实际自振特性与有限元模型理论计算结果吻合良好,为以后桥梁定期检测、状态评估和日常养护提供基准参数。文章内容可为同类型桥梁提供参考。

EXTRACTING MODAL PARAMETERS FROM STRUCTURES UNDERGOING AMBIENT EXCITATION

On Line Parameter Identification Technique (OLPIT) was presented according to ambient excitation characteristics and the response cross-correlation function that is a sum of decaying sinusoids of the same form as the impulse response function of the original system. OLPIT is a new method of identification modal parameters from response of structures under ambient excitation. OLPIT is different from NExT (natural excitation technique) based on ITD method in four aspects: ① The algorithm is improved by the singular-value decomposition (SVD). ② Multi-value of b r in the Ibrahim Time Domain (ITD) is avoided. ③ OLPIT is used in both SIMO (single input, multi-output) and MIMO (multi input, multi-output). ④ The precision of modal parameter identificatioin is improved. The simulation studies demonstrate that the method is effective in identifying complex modes even with close frequencies and is robust to measurement noise.

Ambient vibration testing and updating of the finite element model of a simply supported beam bridge

An ambient vibration test on a concrete bridge constructed in 1971 and calibration of its finite element model are presented.The bridge is characterized by a system of post-tensioned and simply supported beams.The dynamic characteristics of the bridge,i.e.natural frequencies,mode shapes and damping ratios were computed from the ambient vibration tests by using the Eigensystem Realization Algorithm(ERA).Then,these characteristics were used to update the finite element model of the bridge by formulating an optimization problem and then using Genetic Algorithms(GA)to solve it.From the results of the ambient vibration test of this type of bridge,it is concluded that two-dimensional mode shapes exist:in the longitudinal and transverse;and these experimentally obtained dynamic characteristics were also achieved in the analytical model through updating.The application of GAs as optimization techniques showed great versatility to optimize any number and type of variables in the model.