scholarly journals Application of adaptive chirp mode decomposition on parameter identification of sub/super‐synchronous oscillation signals

2021 ◽  
Author(s):  
Xiaomei Yang ◽  
Haoyi Li ◽  
Yubo Mei ◽  
Xunyong Hu ◽  
Yang Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Synchronous Oscillation ◽  
Mode Decomposition

A Combined Method for Time-Varying Parameter Identification Based on Variational Mode Decomposition and Generalized Morse Wavelet

2020 ◽  
Vol 20 (07) ◽  
pp. 2050077
Author(s):  
Chao Wang ◽  
Jing Zhang ◽  
Hong Pin Zhu
Keyword(s):  
Parameter Identification ◽  
Structural Response ◽  
Time Varying ◽  
Variational Mode Decomposition ◽  
Combined Method ◽  
Moving Vehicle ◽  
Mode Decomposition ◽  
Time Varying Parameter ◽  
Time Varying Systems ◽  
And Performance

Time-varying parameter identification is essential for structural health monitoring and performance evaluation. In this paper, a combined method based on the variational mode decomposition and generalized Morse wavelet is proposed to identify the structural time-varying parameters. Based on the sparse property of structural response signals in wavelet domain, a fast iterative shrinkage-thresholding algorithm is adopted to reduce the noise. Then the de-noised signal is decomposed into multi- modes by the variational mode decomposition, and the generalized Morse wavelet is performed to identify the instantaneous frequency. To validate the proposed method, a numerical example including different frequency variations is studied. Experimental validations of a moving vehicle across a bridge and a time-varying cable system considering two patterns of cable tension variations in the laboratory are carried out to investigate the capability of the proposed approach. It is confirmed that the proposed approach can effectively perform the signal decomposition, while identifying the instantaneous frequencies of the time-varying systems accurately.

APPLICATION OF EMPIRICAL MODE DECOMPOSITION IN STRUCTURAL HEALTH MONITORING: SOME EXPERIENCE

2009 ◽  
Vol 01 (04) ◽  
pp. 601-621 ◽  
Author(s):  
JUN CHEN
Keyword(s):  
Structural Health Monitoring ◽  
Data Processing ◽  
Parameter Identification ◽  
Empirical Mode Decomposition ◽  
Health Monitoring ◽  
Structural Parameter ◽  
Raw Data ◽  
Structural Health ◽  
Long Span ◽  
Mode Decomposition

The installation of long-term structural health monitoring (SHM) system on super-tall buildings, long span bridges and large space structures has become a worldwide trend since last decade to monitor loading conditions, to detect damage, to assess structural safety and to guide maintenance during their service life. The core part of an SHM system is the function of data processing and structural parameter/damage identification that extracts useful information from huge amount of raw data and provides reliable knowledge for proper decision. Recently emerged data processing technique empirical mode decomposition (EMD) in conjunction with Hilbert transform (HT) provides a more better and powerful tool for SHM. This paper summarizes some research experience gained from application of EMD + HT in SHM with focuses on pre-processing raw data, structural parameter identification and damage detection. In particular, EMD is applied to determining time varying mean wind speed for wind data and to extract multipath effect from GPS data. For structural parameter identification, the EMD + HT approach is employed to identify natural frequencies and modal damping ratios of long span bridge during passage of strong typhoon and of structures with closely spaced modes of vibration. The results manifest the advantages of EMD + HT over traditional FFT-based methods in damping estimation. Furthermore, experimental investigation has been carried out to study the applicability of EMD for identifying structural damage caused by a sudden change of structural stiffness. It is concluded from all these investigations that EMD approach is a promising tool for structural health monitoring of large civil structures. Finally, some issues concerned for further practical application of EMD are highlighted and discussed based on these academic researches.

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