On the denoising of structural vibration response records from low-cost sensors: a critical comparison and assessment

Abstract

Due to the increasing quest of adopting low-cost sensors in structural health monitoring (SHM) processes, which may lead to detecting signals contaminated by significant levels of noise, the need to devise appropriate and effective denoising strategies, at the post-processing stage, is becoming more and more essential. Among several approaches proposed in the literature, it has been demonstrated that the employment of discrete wavelet transform (DWT) as a multi-rate filter bank, as well as the use of singular value decomposition (SVD), may result to be quite effective in signal denoising within various research fields, as biological, acoustic and mechanical. Here, DWT- and SVD-based denoising techniques are first independently reconsidered and reimplemented, aiming at exploring their optimal calibration in purifying noise-corrupted vibration response signals encountered in civil engineering applications. Then, a systematic performance evaluation is provided within a comparative framework, developed at an increasing level of noise affecting the measurements, in terms of noise-to-signal (N/S) ratio. In the study, two specific classes of synthetic response signals are first considered, namely earthquake and ambient vibration signals, since they may be assumed as representative of more general non-stationary and stationary signal typologies, respectively. To achieve a complete description of the clarified signal, strengths and weaknesses of the two denoising approaches are explored, in both time and frequency domains. The results prove the effectiveness of the analyzed implementations, especially in purifying seismic response signals, while some limitations may arise concerning the treatment of ambient vibration signals, in particular for the DWT-based denoising technique. Finally, a real case study is analyzed, where both denoising approaches are adapted and employed for clarifying acceleration signals detected on a modern short-span railway bridge, with rather satisfactory results, for both techniques.

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

“…where ψ(t) is assumed as a wavelet "mother" function (with overbar symbol for complex conjugate), defined on the Lebesgue space of the 2-norm L 2 (R), having R the set of real numbers. Different mother wavelet types have been extensively evaluated and discussed in [11], within a specific context of signal denoising, showing that a pertinent choice for handling non-stationary signals shall be that of a Symlet. In the current application, the mother wavelet is implemented through a band-pass Butterworth filter (see, e.g., [40]), consistently with the Shannon "sinc" wavelet approach [18].…”

“…The bridge has been the subject of a comprehensive investigation in the recent years [3], toward assessing its current functioning conditions, based on an experimental campaign of response data acquisition and processing, with the target of developing integrated methodological approaches of post-processing analysis, based on Heterogeneous Data Fusion [4][5][6], non-empirical, i.e., algorithmic, FEM model updating [7,8], and signal denoising [9][10][11].…”

“…Specifically, a Wavelet technique (see, e.g., [17,18]) is selected, as an efficient approach for this purpose. Being already successfully applied in the application fields of damage identification (see, e.g., [19][20][21]) and signal denoising (see, e.g., [9][10][11]22]), a Wavelet analysis can be specifically considered in bridge signal processing, as e.g., proposed in [23][24][25][26][27][28][29]. It is here applied to the same two channels of each side of the central span of the bridge only, assuming that the outcomes could easily be extended to the channels of the other two spans.…”

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

“…At last, the denoised signal was obtained through reconstruction. Based on the denoising results presented in Figure 8, In order to quantitatively analyze the denoising effect of the proposed method, we took signal-to-noise ratio (SNR) and Mean Square Error (MSE) as the indicators to evaluate the denoising effect with denoised signals and noisecontaining signals [34]. The greater SNR, the better effect of denoising.…”

An Improved Wavelet Threshold Denoising Method for Health Monitoring Data: A Case Study of the Hong Kong-Zhuhai-Macao Bridge Immersed Tunnel

“…For the problem of denoising health monitoring data, a lot of research points to the wavelet analysis method. These methods include the Bayesian discrete wavelet packet transform denoising approach [16], an adaptive wavelet packet denoising algorithm [17], an improved wavelet threshold denoising method [18], and discrete wavelet transform (DWT)-based denoising techniques [19]. These wavelet analysis-based methods each have their own characteristics and advantages, but they also have some limitations.…”