An Improved Second-Order Blind Identification (SOBI) Signal De-Noising Method for Dynamic Deflection Measurements of Bridges Using Ground-Based Synthetic Aperture Radar (GBSAR)

Abstract

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR-especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles-an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.technology with the advantages of real-time monitoring, high-distance resolution, fast measurement speed, high precision, wide measurement coverage, easy operation, and all-weather and all-day measurements [3,4]. Since the first published paper (in 1999) on microwave interferometry for non-contact vibration measurements on large structures [5], GBSAR has been used extensively for dynamic deflection measurements of bridges in recent years [6,7]. However, during the data acquisition process of dynamic deflection measurements of bridges using GBSAR, the surrounding environment, human operation, and the equipment itself will inevitably increase noise in the obtained time-series displacements, which reduces the damage detection accuracy [8]. In particular, the used GBSAR equipment should be placed under the monitored bridge, and the instantaneous vibrations of the equipment itself will be inevitably caused by passing vehicles under the monitored bridge, which will reduce the accuracy of the obtained dynamic time-series displacement. Therefore, it is of great importance to reduce the influence of noise information in the time-series displacements of bridges obtained using GBSAR-especially for the instantaneous vibrations of the equipment itself.Currently, the primary signal de-noising methods for the time-series data include the filtering method [9][10][11][12], the wavelet transform method [13...

Ses İşaretlerinin Ayrıştırılmasında Kör Kaynak Algoritmalarının Performans Analizleri

“…İkinci dereceden kör tanımlama (SOBI) yöntemi, kaynak sinyali ve karıştırma matrisinin en iyi tahminini çözebilen ve ayrıca farklı kaynak sinyallerinden ayrılmış sinyal bileşenlerini elde edebilen orijinal gözlem verilerinin ikinci dereceden istatistiklerine dayanan kör kaynak ayırma yöntemidir (Belouchrani, Abed-Meraim, Cardoso, & Moulines, 1997). SOBI yöntemi, kaynak sinyal bileşenini tahmin edebilen ve göreceli olarak daha az veri noktası kullanarak birden fazla Gauss gürültü kaynağını ayırabilmektedir (Liu, Wang, Huang, & Yang, 2019). Bir dizi çalışmada modal parametre tanımlaması için umut verici bir alternatif olarak kabul edilmiştir (Rainieri, 2014).…”

Integration of Extreme-point Symmetric Mode Decomposition and Singular Value Decomposition for Dynamic Deflection Denoising of Urban Bridges Obtained by Ground-based Microwave Interferometry

“…(9)(10)(11)(12) In WT noise reduction, it is necessary to manually select the appropriate wavelet base, the number of wavelet decomposition layers, and the noise reduction threshold, which cause a lack of adaptability. (13,14) In addition, WT is not suitable for nonlinear signals, which means that it is not suitable for analyzing the dynamic deflection signals of monitored urban bridges. If a monitored bridge is damaged, the obtained dynamic deflection should be a typical nonlinear and nonstationary signal.…”

“…If a monitored bridge is damaged, the obtained dynamic deflection should be a typical nonlinear and nonstationary signal. (14,15) EMD is an adaptive decomposition method proposed by Huang et al in 1998, which overcomes the limitations of wavelet analysis. (16) It can adaptively decompose a signal into a series of intrinsic mode functions (IMFs) of different scales without artificially selecting the basis function and can achieve the adaptive filtering of the signal.…”

“…It has been widely used in the field of vibration signal denoising. (14,15) However, EMD can cause the mode mixing problem, which means that signals of different frequencies or scales appear in the same IMF component, or signals of the same scale or frequency are automatically decomposed into multiple different IMF components. (15,17) Moreover, the effect of this recursive mode is affected by the envelope mode and there is a problem of a rough trend function.…”

“…It is an improved method based on EMD and EEMD, and is being increasingly applied in fields including mechanical vibration, marine and atmospheric sciences, and seismology. (1,4,14,15,30) ESMD uses direct interpolation (DI) for spectrum analysis, which can not only intuitively reflect the time-varying amplitude and frequency of each mode, but also clarify the instantaneous energy change. (1,14,15) Moreover, this method uses the least-squares method to optimize the final residual mode to obtain the best adaptive global mean (AGM) curve.…”