Parameter identification of fluid line networks by frequency-domain maximum likelihood estimation

Zecchin, Aaron C.; White, L.B.; Lambert, Martin F.; Simpson, Angus R.

doi:10.1016/j.ymssp.2013.01.003

Cited by 46 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the last decade, the analysis of the FRF of pipeline systems under linear system theory has also been used in integrity monitoring of water pipelines, such as the detection of leaks (Covas, Ramos, & de Almeida, 2005;Duan, Lee, Ghidaoui, & Tung, 2011;, 2014Lee, Vítkovský, Lambert, Simpson, & Liggett, 2005a, 2005bMpesha, Gassman, & Chaudhry, 2001;, discrete blockages (Lee, Vítkovský, Lambert, Simpson, & Liggett, 2008;Sattar, Chaudhry, & Kassem, 2008), extended blockages (Duan, Lee, Ghidaoui, & Tung, 2012, 2013, and general parameter identification (Zecchin, White, Lambert, & Simpson, 2013).…”

Section: Introductionmentioning

confidence: 99%

Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal

Gong

Lambert

Zecchin

et al. 2015

Journal of Hydraulic Research

Self Cite

View full text Add to dashboard Cite

This paper presents the original design of a side-discharge valve based transient generator that can generate two types of pseudorandom binary signals: the maximum length binary signal and the inverse repeat signal (IRS). These two signals are both wide bandwidth, persistent and periodic, but the IRS has the advantageous property that it is antisymmetric within each period. The two signals are used to extract the frequency response function (FRF) of a single water pipeline in the laboratory. The experimental results demonstrate that the FRF extracted by the IRS is closer to the theoretical linear results as obtained from the transfer matrix method due to it being able to cancel the effect of even-order nonlinearities. The customised transient generator is then applied to a pipeline with a leak. The location of the leak is successfully determined using the first three resonant peaks as extracted by the IRS.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal

Gong

Lambert

Zecchin

et al. 2015

Journal of Hydraulic Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, it provides a reliable model to describe the current state of the structure [1]. Structural parameter identification is based on least squares or maximum likelihood criterion generally [2,3], which requires solving a nonlinear and nonconvex optimization problem. The error surface in this estimation problem is a hypersurface in the multidimensional parameter space, thereby the hypersurface potential is highly multimodal possessing local optimum.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

et al. 2019

View full text Add to dashboard Cite

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

show abstract

“…12 Many researchers have made a lot of efforts in the identification of structural parameters with different methods such as Bayesian network, Kalman filter, Likelihood estimation. 13–18 Udwadia et al 19 put forward that the stiffness and damping of a building story can be uniquely identified with the conditions of the recorded absolute accelerations. Takewaki and Nakamura 20 taken advantage of the limited earthquake records and the knowledge of the floor masses to identify the stiffness and damping of building structures.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Xue

Wen

Huang

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

show abstract

Parameter identification of fluid line networks by frequency-domain maximum likelihood estimation

Cited by 46 publications

References 28 publications

Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal

Experimental verification of pipeline frequency response extraction and leak detection using the inverse repeat signal

Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

Contact Info

Product

Resources

About