Numerical and Experimental Investigation on Parameter Identification of Time‐Varying Dynamical System Using Hilbert Transform and Empirical Mode Decomposition

Zhao, Guangjun

doi:10.1155/2014/568637

Cited by 5 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 2, the instantaneous frequency values obtained from the three methods were compared with the theoretical values at several times and the error was calculated at each time in percentage terms. Note that the theoretical values of the frequencies at the specified time in Table 2 are calculated accurately by the analytical methods available in the signal processing (Chen and Zhao, 2014). Table 2 shows that the LMD method can extract frequencies more accurately than the other two methods.…”

Section: Comparison Of the Three Signal Processing Methods Emd Lmd mentioning

confidence: 99%

Damage detection of structures using signal processing and artificial neural networks

Aval

Ahmadian

Maldar

et al. 2019

Advances in Structural Engineering

View full text Add to dashboard Cite

This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.

show abstract

Section: Comparison Of the Three Signal Processing Methods Emd Lmd mentioning

confidence: 99%

Damage detection of structures using signal processing and artificial neural networks

Aval

Ahmadian

Maldar

et al. 2019

Advances in Structural Engineering

View full text Add to dashboard Cite

show abstract

“…Over the past few decades, several parameter identifcation technologies have been developed to address the challenge of identifying time-variant parameters in nonlinear structures. Representative researches mainly include time, frequency, and time-frequency domain signal processing methods [3][4][5][6][7][8][9]. In the time-frequency domain methods, the wavelet multiresolution analysis (WMA), Hilbert transform (HT), Hilbert-Huang transform (HHT), and variational mode decomposition (VMD) are fully developed for diferent applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification

Zhang,

Ding,

et al. 2023

Structural Control and Health Monitoring

View full text Add to dashboard Cite

The unscented Kalman filter (UKF) serves as an efficient estimator widely utilized for the recursive identification of parameters. However, the UKF is not well suited for tracking time-variant parameters. Moreover, the unscented transformation (UT) used in the UKF typically relies on Cholesky decomposition to perform the square root operation of the covariance matrix. This method necessitates the matrix to maintain symmetry and positive definiteness. Due to the adverse influence of rounding error and noise, it becomes challenging to guarantee the positive definiteness of the matrix in each recursive step for practical engineering. The square root UKF (SRUKF) eliminates the need for the square root operation in the UT by directly updating the square root of the covariance matrix during each recursion. However, the SRUKF still relies on the rank 1 update to the Cholesky factorization to perform the recursive process, which also necessitates the matrix to be positive definite. Furthermore, the SRUKF is ineffective in the identification of time-variant parameters. Therefore, this paper proposes a modification to the SRUKF that ensures unconditional numerical stability by utilizing QR decomposition. Subsequently, the modified square root UKF (MSRUKF) method is enhanced by incorporating an adaptive forgetting factor that can be adjusted based on the residual information from each recursive step. This adaptation leads to the development of the adaptive SRUKF with forgetting factor (ASRUKF-FF) method, which significantly improves the tracking capability for time-variant parameters. To validate the effectiveness of the proposed method, this paper demonstrates its application in identifying the time-variant stiffness and damping parameters of a three-story frame structure. In addition, the method is employed to estimate the time-variant stiffness of the bridge excited by vehicles. The simulation results show that the proposed method has the superiority of high accuracy, strong robustness, and widespread applicability, even with incomplete measurements and inappropriate parameter settings.

show abstract

“…Therefore, it is essential to research on the identification of time‐varying systems . However, as indicated in the state‐of‐the‐art review by Wang et al, identification of time‐varying structural system is less established than that of time‐invariant structures, although some time‐frequency analysis tools such as Hilbert transform and Hilbert–Huang transform, variational mode decomposition have been developed for identification of time‐varying structural systems, these tools can only provide empirical information. Also, due to its strong capability, wavelet transform has been utilized to identify the time‐varying structural parameters .…”

Section: Introductionmentioning

confidence: 99%

Synthesize identification and control for smart structures with time‐varying parameters under unknown earthquake excitation

Lei

Huang

2020

Struct Control Health Monit

View full text Add to dashboard Cite

Summary The synthesis of structural identification with vibration control is cost‐effective and beneficial for developing smart building structures. In the past several decades, techniques have been put forward for the combination of structural identification and vibration control. However, it is still a challenging task to synthesize identification and vibration control of time‐varying structures under unknown earthquake excitation. First, structural dynamic responses collected by a structural health monitoring (SHM) system are absolute responses under unknown earthquake excitation, so existing identification approaches for unknown external excitations are not applicable for this situation. Moreover, it is essential to have an efficient algorithm for accurately tracking the various scenarios of time‐varying structural physical parameters with inexpensive computation to ensure the real‐time performance requested by structural vibration control. In this paper, an algorithm is put forward to tackle this challenging problem. It is proposed to treat structural time‐varying effect as “virtual unknown inputs” to the corresponding time‐invariant structure. A generalized extended Kalman filtering with unknown input (GEKF‐UI) is proposed to circumvent the limitations of the existing EKF‐UI approaches. The proposed GEKF‐UI can simultaneously identify structural system, unknown earthquake excitation, and the “virtual unknown inputs” using only partially measured structural absolute responses. Then, the identified structural state is synthesized in real time with the instantaneous optimal control strategy for optimal semiactive control provided by magnetorheological dampers. Therefore, the proposed algorithm can track various structural time‐varying with much less computation, which is more suitable for synthesis with structural control compared with other existing approaches. Some numerical cases of synthesizing identification and vibration control of various type time‐varying structures under unknown earthquake motion are adopted to investigate the feasibilities of the proposed algorithm.

show abstract

Numerical and Experimental Investigation on Parameter Identification of Time‐Varying Dynamical System Using Hilbert Transform and Empirical Mode Decomposition

Cited by 5 publications

References 13 publications

Damage detection of structures using signal processing and artificial neural networks

Damage detection of structures using signal processing and artificial neural networks

A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification

Synthesize identification and control for smart structures with time‐varying parameters under unknown earthquake excitation

Contact Info

Product

Resources

About