scholarly journals System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

Machines ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 12 ◽  
Author(s):  
◽  
Keyword(s):  
System Identification ◽  
Mechanical Systems ◽  
Modal Parameters ◽  
Identification Algorithm

Novel tensor subspace system identification algorithm to identify time-varying modal parameters of bridge structures

2021 ◽  
pp. 147592172110360
Author(s):  
Erhua Zhang ◽  
Di Wu ◽  
Deshan Shan
Keyword(s):  
System Identification ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Time Varying ◽  
Advanced Technique ◽  
Stabilization Diagram ◽  
Bridge Structures ◽  
Tensor Subspace

Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.

Identification of Modal Parameters of an Elastic Rotor With Oil Film Bearings

1984 ◽  
Vol 106 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Rainer Nordmann
Keyword(s):  
Modal Analysis ◽  
Dynamic Behavior ◽  
Design Process ◽  
Mechanical Engineering ◽  
Mechanical Systems ◽  
Rotating Machinery ◽  
Modal Parameters ◽  
Powerful Method ◽  
Oil Film ◽  
Engineering Problems

Investigations of the dynamic behavior of structures have become increasingly important in the design process of mechanical systems. To have a better understanding of the dynamic behavior of a structure, the knowledge of the modal parameters is very important. The powerful method of experimental modal analysis has been used to measure modal parameters in many mechanical engineering problems. But the method was mainly applied to nonrotating structures. This presentation shows improvements of the classical modal analysis for a successful application in rotating machinery with nonconservative effects. An example is given, investigating the modal parameters of an elastic rotor with oil film bearings.

scholarly journals Nonlinear Aeroelastic System Identification Based on Neural Network

2018 ◽  
Vol 8 (10) ◽  
pp. 1916
Author(s):  
Bo Zhang ◽  
Jinglong Han ◽  
Haiwei Yun ◽  
Xiaomao Chen
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
System Identification ◽  
Control Surface ◽  
Identification Algorithm ◽  
Aeroelastic System ◽  
Structural Nonlinearity ◽  
Time Marching ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

Research of Fan System Identification Based on Improved Bayes Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 448-453
Author(s):  
Hai Tao Wang ◽  
Ze Zhang
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
System Modeling ◽  
Estimation Algorithm ◽  
Parametric Model ◽  
Testing System ◽  
Identification Algorithm ◽  
Environment Protection ◽  
Parameter Estimation Algorithm ◽  
Bayes Algorithm

In every filed of natural science, more and more researchers attach importance to system quantitative analysis, control and prediction. In filed of automatic control, system identification is the extension of system dynamic characteristics testing. System modeling is the basis of system identification, non-parametric model can be obtained by means of dynamic characteristics testing, but parametric model must be established by means of parameter estimation algorithm, which is more prevalent than dynamic characteristics testing. Coal power plant produces more gas and dust, so how to control the fan system plays a very important role in environment protection. We must clarify the parameter of fan system before controlling it. The traditional Bayes identification algorithm is used widely in research and industry, and the effect is relatively good. The paper induces the concept of loss function based on traditional Bayes identification algorithm, and proposes an improved Bayes identification algorithm, which can be applied to fan system identification successfully.

Automated modal parameters identification of bistable composite plate using two-stage clustering of operational modal testing

2020 ◽  
pp. 002199832090308
Author(s):  
Mahdi Ghamami ◽  
Hassan Nahvi ◽  
Vahid Yaghoubi
Keyword(s):  
Analytical Method ◽  
Composite Plate ◽  
Ritz Method ◽  
Stable State ◽  
User Interaction ◽  
Modal Testing ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Stable States ◽  
Modal Parameters Identification

In recent years, smart structures have attracted much interest as morphing structures. One of the simplest types of these structures is bistable composite plate, which has many applications in aerospace, structures, actuators, etc. On the other hand, inverse problem theory provides conceptual ideas and methods for the practical solution of applied problems. These methods are opposite of the forward problem and define a model of the system based on output or observations. In this paper, a modified identification algorithm is used to determine the modal parameters of a bistable composite plate based on vibrational signals. Both analytical and experimental approaches have been considered and analytical method has been used to investigate the accuracy of identification algorithm, which has been performed based on experimental measurement. In the analytical method, static and free vibration behaviors of a cross-ply bistable composite plate are studied by the Hamilton's principle and the Rayleigh–Ritz method. The experimental approach is performed by an operational modal testing, which is a nondestructive test. The identification process does not require user interaction and the process uses only a single dataset and there is no need to repeat the test or data collection. The advantages of the proposed algorithm is the ability to determine the modal parameters of each stable state with high accuracy and robustness. A comparison of the natural frequencies shows that the identification of both stable states has been successful and the estimated modal parameters are in good agreement with the analytical and experimental results.

scholarly journals A Method of Modal Parameter Identification for Wind Turbine Blade Based on Binocular Dynamic Photogrammetry

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Wenyun Wang ◽  
Xuejun Li ◽  
Anhua Chen
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Modal Identification ◽  
Image Feature ◽  
Vibration Measurement ◽  
Modal Parameters ◽  
Target Point ◽  
Identification Algorithm ◽  
Modal Parameter

The identification of operational modal parameters of a wind turbine blade is fundamental for online damage detection. In this paper, we use binocular photogrammetry technology instead of traditional contact sensors to measure the vibration of blade and apply the advanced stochastic system identification technique to identify the blade modal frequencies automatically when only output data are available. Image feature extraction and target point tracking (PT) are carried out to acquire the displacement of labeled targets on the wind turbine blade. The vibration responses of the target points are obtained. The data-driven stochastic subspace identification (SSI-Data) method based on the Kalman filter prediction sequence is explored to extract modal parameters from vibration response under unknown excitation. Hankel matrixes are reconstructed with different dimensions, so different modal parameters are produced. Similarity of these modal parameters is compared and used to cluster modes into groups. Under appropriate tolerance thresholds, spurious modes can be eliminated. Experiment results show that good effects and stable accuracy can also be achieved with the presented photogrammetry vibration measurement and automatic modal identification algorithm.

scholarly journals Automated Harmonic Signal Removal Technique Using Stochastic Subspace-Based Image Feature Extraction

2020 ◽  
Vol 6 (3) ◽  
pp. 10
Author(s):  
Muhammad Danial Bin Abu Hasan ◽  
Zair Asrar Bin Ahmad ◽  
Mohd Salman Leong ◽  
Lim Meng Hee
Keyword(s):  
Feature Extraction ◽  
System Identification ◽  
Output Signal ◽  
Parameters Estimation ◽  
Human Interaction ◽  
Image Feature ◽  
Modal Parameters ◽  
Start Up ◽  
Stabilization Diagram ◽  
Harmonic Components

This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal.

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