Model order determination and noise removal for modal parameter estimation

Hu, Sau‐Lon James; Bao, Xingxian; Li, Huajun

doi:10.1016/j.ymssp.2010.01.005

Cited by 27 publications

(9 citation statements)

References 19 publications

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“…For theoretical data, the singular values should go to zero when the rank of the matrix is exceeded. For measured data, however, due to random errors and small inconsistencies in the data, the singular values will not become zero but will become very small [14]. The normalized singular values of ten Hankel matrices H 151 151 ´are presented in figure 1(a).…”

Section: Resultsmentioning

confidence: 99%

“…Modal parameters are considered to be global properties of the system, and theoretically they should remain the same when they are estimated from different IRFs [14,15]. Although the true values of the modal parameters of the test beam are not we could still judge the performance of the proposed scheme based on examining the consistency among the modal parameters estimated from these different IRFs.…”

Section: Resultsmentioning

confidence: 99%

“…Sanliturk and Cakar [13] proposed a method based on singular value decomposition (SVD) for the elimination of noise from measured FRFs so as to improve the quality of the measured data. Hu et al [14,15] treated the noise elimination algorithm as an optimization problem and Cadzow's algorithm [16] for structured low rank approximation for the Hankel matrix was carried out. Cadzow's algorithm is an iterative method that alternates between unstructured low rank approximation (truncated SVD) and structure enforcement via anti-subdiagonal averaging, thereby only requiring computation related to SVD and manipulation of the matrix entries.…”

Section: Introductionmentioning

confidence: 99%

“…The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data and the rank constraint is related to a bound on the model order [14]. Once the filtered IRF is obtained, the CE method, can be applied to extract the modal parameters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Noise reduction for modal parameters estimation using algorithm of solving partially described inverse singular value problem

Bao

Cao

Zhang

2016

Smart Mater. Struct.

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Modal parameters estimation plays an important role for structural health monitoring. Accurately estimating the modal parameters of structures is more challenging as the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of solving the partially described inverse singular value problem (PDISVP) combined with the complex exponential (CE) method to estimate the modal parameters. The PDISVP solving method is to reconstruct an L 2 -norm optimized (filtered) data matrix from the measured (noisy) data matrix, when the prescribed data constraints are one or several sets of singular triplets of the matrix. The measured data matrix is Hankel structured, which is constructed based on the measured impulse response function (IRF). The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. Once the filtered IRF is obtained, the CE method can be applied to extract the modal parameters. Two physical experiments, including a steel cantilever beam with 10 accelerometers mounted, and a steel plate with 30 accelerometers mounted, excited by an impulsive load, respectively, are investigated to test the applicability of the proposed scheme. In addition, the consistency diagram is proposed to exam the agreement among the modal parameters estimated from those different accelerometers. Results indicate that the PDISVP-CE method can significantly remove noise from measured signals and accurately estimate the modal frequencies and damping ratios.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Noise reduction for modal parameters estimation using algorithm of solving partially described inverse singular value problem

Bao

Cao

Zhang

2016

Smart Mater. Struct.

Self Cite

View full text Add to dashboard Cite

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“…Alamdari et al [3] introduced a Gaussian kernel algorithm to reduce unnecessary noise from noisy FRFs, and it is designed to localize damage in the presence of heavy noise influences by using FRFs of the damaged structure only. Hu et al and Bao et al [4,5] introduced a Cadzow's algorithm to reduce unnecessary noise from noisy FRFs, but the denoising method needs to set a reasonable noise threshold on the basis of the measured signals. The effectiveness of the denoising methods in [3][4][5] was illustrated by simulation and experimental data, but none of the results shows that these denoising methods can remove strong background noise mixed in a forced response signal.…”

Section: Introductionmentioning

confidence: 99%

Identification of modal parameters from noisy transient response signals

Wang

Friswell

et al. 2017

Struct Control Health Monit

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Summary In the process of impact testing of large‐scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal‐to‐noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this problem, the mean averaging of repeatedly measured frequency response function estimates is often employed in practical applications. However, a large number of impact tests are not practical for the modal testing of large‐scale mechanical equipment. The primary objective of this paper is to reduce the averaging operation and improve the accuracy of the modal identification by using a noise removal technique. A hybrid denoising method is proposed by combining the Wiener and improved minimum mean‐square‐error short‐time spectral amplitude estimators. The proposed method can effectively remove both stationary and highly nonstationary noise while preserving the important features of the true forced response signals. The simulation results show that the proposed noise removal technique improves the accuracy of the estimated modal parameters using only one impulse response signal. The experimental results show that the proposed method can accurately identify a natural frequency that is very close to a strong interference frequency in the modal test of a 600‐MW generator casing.

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Analysis and Correction of Ill-Conditioned Model in Multivariable Model Predictive Control

Peng

Huang

Zou

et al. 2015

Intelligent Robotics and Applications

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Ill-conditioned model usually appears in modelling high purity and complicated production processes such as the high-purity distillation column. Model predictive control (MPC) via closed-loop feedback is a class of methods for which a plant model is used to forecast the outputs or states of controlled systems and to then solve a linear or nonlinear programming with multivariable and constraints by so-called receding horizon optimisation. Ill-conditions can cause many negative effects on the implementation of MPC including system instability and controller failure. In this paper, these phenomena such as output static error and stability decreasing of ill-conditioned model caused in MPC are found by simulating simple examples, and the close correlation between the movement direction of the controlled system output and the characteristics of ill-conditioned model is also observed. The geometry tools and singular value decomposition (SVD) in linear algebra are used to analyse the essential cause of ill-conditioned model generation. A new offline quantitative strategy is proposed to improve the ill-conditioned model. Based on modified model of reengineering and implementation of model predictive control, the closed-loop control performance and stability can be significantly enhanced.

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Model order determination and noise removal for modal parameter estimation

Cited by 27 publications

References 19 publications

Noise reduction for modal parameters estimation using algorithm of solving partially described inverse singular value problem

Noise reduction for modal parameters estimation using algorithm of solving partially described inverse singular value problem

Identification of modal parameters from noisy transient response signals

Analysis and Correction of Ill-Conditioned Model in Multivariable Model Predictive Control

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