scholarly journals Flutter Test Data Processing Based on Improved Hilbert-Huang Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Hua Zheng ◽  
Junhao Liu ◽  
Shiqiang Duan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Model Parameters ◽  
Modal Parameter ◽  
Hilbert Huang Transform ◽  
Modal Density ◽  
Flutter Boundary ◽  
Modal Parameter Estimation ◽  
Nonstationary Conditions ◽  
Typical Mode

Flutter tests are conducted primarily for the purpose of modal parameter estimation and flutter boundary prediction, the accuracy of which is severely affected by the acquired data quality, structural modal density, and nonstationary conditions. An improved Hilbert-Huang Transform (HHT) algorithm is presented in this paper which mitigates the typical mode mixing effect via modulation. The algorithm is validated by theory, by numerical simulation, and per actual flight flutter test data. The results show that the proposed method could extract the flutter model parameters and predict the flutter speed more accurately, which is feasible for the current flutter test data processing.

scholarly journals Efficient Use of the Output Information to Improve Modal Parameter Estimation

Proceedings ◽  
2018 ◽  
Vol 2 (8) ◽  
pp. 519
Author(s):  
Oscar Olarte ◽  
Mahmoud El-Kafafy ◽  
Patrick Guillaume
Keyword(s):  
Model Identification ◽  
Modal Identification ◽  
Model Parameters ◽  
Modal Parameter ◽  
Likelihood Estimator ◽  
True Value ◽  
Output Information ◽  
Modal Parameter Estimation ◽  
Associated Data

In modal identification, the value of the model parameters and the associated uncertainty depends on the quality of the measurements. The maximum likelihood estimator (mle) is a consistent and efficient estimator. This means that the value of the parameters trends asymptotically close to the true value, while the variance of such parameters is the lowest possible with the associated data. The mle implementation and application can be complex and generally need strong computational requirements. In applications where the number of inputs and outputs are elevated (as in modal analysis) is common to reduce the covariance matrix to a diagonal one where only the variances are considered. This implementation is still consistent but not efficient. However, it generates acceptable results. The current work shows that using efficiently the output information as complement to the input–output relations, it is possible to improve the model identification reaching similar levels than the mle, while reducing the execution time and the computational load.

Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam

2008 ◽  
Vol 56 ◽  
pp. 453-458
Author(s):  
John D. Hios ◽  
Spilios D. Fassois
Keyword(s):  
Composite Beam ◽  
High Accuracy ◽  
Modal Parameters ◽  
Model Parameters ◽  
Smart Composite ◽  
Modal Parameter ◽  
Parameter Uncertainties ◽  
Vector Autoregressive ◽  
Stochastic Vector ◽  
Modal Parameter Estimation

This study aims at identifying the modal characteristics and their uncertainties for a smart composite beam. The problem is addressed via Vector AutoRegressive with eXogenous excitation (VARX) models. The advantages of VARX modeling include simplicity of implementation, high accuracy, parsimony of representation, and capability of handling modal uncertainties. Two different approaches to assess the modal parameter uncertainties are investigated. The first is based upon linearizing the function that relates the VARX model parameters with the modal parameters, whereas the second is based upon computer simulations using the Monte Carlo and the bootstrap schemes. The results indicate that VARX modeling captures the system dynamics and provides accurate modal parameters with tight confidence intervals.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Design of Road Test and Evaluation System for Vehicle Handling and Stability

2014 ◽  
Vol 505-506 ◽  
pp. 281-285
Author(s):  
Ming Qiu Gao ◽  
Run Qing Guo ◽  
Rong Liang Liang
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Evaluation System ◽  
Test System ◽  
Stability Test ◽  
Road Test ◽  
Vehicle Handling ◽  
Test And Evaluation ◽  
Test Result

Vehicle handling and stability has effect on positive safety of automotive directly. Test system of handling and stability is built for its road test and the test variables signal can be acquired and stored synchronously. Based on MATLAB GUI, software is developed for the test data processing, so that the stored data is to be analyzed and handling and stability test result is given by the software automatically. Using the test system in paper, handling and stability road test of one domestic sedan is fulfilled and scored, which verifies the applicability of the test system and scoring software in paper.

A Processing Method for Combined Fatigue Accelerated Test Data

2017 ◽  
Author(s):  
Songwang Zheng ◽  
Cao Chen ◽  
Lei Han ◽  
Xiaoyong Zhang ◽  
Xiaojun Yan
Keyword(s):  
Data Processing ◽  
Test Data ◽  
Turbine Blades ◽  
Processing Method ◽  
Test Time ◽  
Accelerated Test ◽  
The Real ◽  
Data Processing Method ◽  
Stress Levels ◽  
Vibration Stress

To carry out combined low and high cycle fatigue (CCF) test on turbine blades in a bench environment, it is imperative to simulate the vibration loads of turbine blades in the field. Due to the low vibration stress of turbine blades in the working state, the test time will be very long if the test vibration stress is equal to the real vibration stress in working state. Therefore, an accelerated test will be used when the test life reach the target value (typically 107). During the accelerated test, each blade is tested at two or more times than the real vibration stress. That means some specimens are tested under two vibration stress levels. In this case, a reasonable data processing method becomes very important. For this reason, a data processing method for the CCF accelerated test is proposed in this paper. These test data are iterated on the basis of S-N curve. Finally, ten real turbine blades are tested in a bench environment, one of them is tested under two vibration stress levels. The test data is processed using the method proposed above to obtain the unaccelerated life data.

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