scholarly journals Detecting the Void behind the Tunnel Lining by Impact-Echo Methods with Different Signal Analysis Approaches

2019 ◽  
Vol 9 (16) ◽  
pp. 3280 ◽  
Author(s):  
Rongning Cao ◽  
Meng Ma ◽  
Ruihua Liang ◽  
Chao Niu
Keyword(s):  
Frequency Domain ◽  
Signal Analysis ◽  
Dynamic Stiffness ◽  
Tunnel Lining ◽  
Impact Echo ◽  
Time Frequency ◽  
Void Detection ◽  
Wavelet Transform Analysis ◽  
The Time Domain ◽  
The Impact

A void behind the lining in a tunnel is considered to be a critical condition as it can significantly impair the tunnel service life. In this study, we adopted the impact-echo (IE) method to detect the voids. We designed two test conditions (tunnel lining with and without a void) for our experiments performed in a laboratory environment. The influences of void size and impact-void position were analysed using numerical simulations. The vibration response signals were analysed in the time, frequency, and time–frequency domains using various signal analysis approaches. The results were comparatively analysed to determine the best approach for void detection. The study helped establish that a tunnel void can be evaluated through the vibration energy (amplitude and duration) in the time domain, the resonance frequency and dynamic stiffness in the frequency domain, and the energy distribution in time–frequency domain. The wavelet transform analysis is the most appropriate method to observe the energy flow during the state changing and the dynamic stiffness method can determine the void position precisely.

scholarly journals Influence of Soil Parameters on Detecting Voids behind a Tunnel Lining Using an Impact Echo Method

2019 ◽  
Vol 9 (24) ◽  
pp. 5403
Author(s):  
Meng Ma ◽  
Rongning Cao ◽  
Chao Niu ◽  
Hougui Zhang ◽  
Weining Liu
Keyword(s):  
Element Model ◽  
Tunnel Lining ◽  
Parameter Analysis ◽  
Soil Parameters ◽  
Impact Echo ◽  
Time Frequency ◽  
Box Counting ◽  
Box Counting Dimension ◽  
Frequency Domains ◽  
The Impact

Tunnel lining cavities are a common problem that may affect the bearing capacity of the tunnel-supporting structure, as well as the tunnel service life. The impact echo (IE) method can be used to detect voids behind tunnel linings. For a long tunnel, the surrounding rocks/soils are inhomogeneous and anisotropic, with parameters that vary with tunnel mileages. It is interesting to analyse whether alterations of the soil parameters affect the non-destructive test results. A laboratory experiment was performed in this study, in which voids behind a concrete plate, representing the tunnel lining, were designed to model the ineffective contact between the soil and the tunnel. The IE method was employed to inspect the existence of the void using different signal analysis approaches in the time, frequency and time–frequency domains. Furthermore, the fractal box-counting dimension was calculated for the purpose of quantitative evaluation. Different soil parameters and void sizes were considered, and finally, a finite element model was built and parameter analysis was accomplished using the software ABAQUS. The results demonstrated that: (1) A comprehensive analysis of vibration signals in the time, frequency and time–frequency domains was useful for identifying voids, while the box-counting dimension was useful for evaluating voids quantitatively. (2) Soils with large density and Young’s modulus differences had a certain influence on void detection, while those with large water content and Poisson’s ratio differences had little influence. (3) The box-counting dimension value was stable within the area where the void existed behind the tunnel; when the detection point was beyond twice that of the void dimension, it was difficult to locate the void.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

Dynamic Response of 3D Surface/Embedded Rigid Foundations of Arbitrary Shapes on Multi-Layered Soils in Time Domain

2019 ◽  
Vol 19 (09) ◽  
pp. 1950106 ◽  
Author(s):  
Zejun Han ◽  
Mi Zhou ◽  
Xiaowen Zhou ◽  
Linqing Yang
Keyword(s):  
Dynamic Response ◽  
Frequency Domain ◽  
Time Domain ◽  
Dynamic Stiffness ◽  
Dynamic Responses ◽  
Rigid Foundation ◽  
Layered Soils ◽  
Stiffness Matrices ◽  
The Time Domain ◽  
Arbitrary Shapes

Significant differences between the predicted and measured dynamic response of 3D rigid foundations on multi-layered soils in the time domain were identified due to the existence of uncertainties, which makes the issue a complicated one. In this study, a numerical method was developed to determine the dynamic responses of 3D rigid surfaces and embedded foundations of arbitrary shapes that are bonded to a multi-layered soil in the time domain. First, the dynamic stiffness matrices of the rigid foundations in the frequency domain are calculated via integral domain transformation. Secondly, a dynamic stiffness equation for rigid foundations in the time domain is established via the mixed variables formulation, which is based on the discrete dynamic stiffness matrices in the frequency domain. The proposed method can be applied to the treatment of systems with multiple degrees of freedom without losing the true information that concerns the coupling characteristics. Numerical examples are presented to demonstrate the accuracy of the proposed method for predicting the horizontal, vertical, rocking, and torsional vibrations. Further, a parametric study was carried out to provide insight into the dynamic behavior of the soil–foundation interaction (SFI) while considering soil nonhomogeneity. The results indicate that the elastic modulus of the soil has a significant impact on the dynamic responses of the rigid foundation. Finally, a numerical example of a rigid foundation resting on a six-layered, semi-infinite soil demonstrates that the proposed method can be used to deal with multi-layered media in the time domain in a relatively easy way.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

scholarly journals An impact echo method to detect cavities between railway track slabs and soil foundation

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
Pierre Anthyme Bahati ◽  
Viet Dinh Le ◽  
Yujin Lim
Keyword(s):  
Open Space ◽  
Concrete Slab ◽  
Test Methods ◽  
Railway Track ◽  
Element Analysis ◽  
Impact Echo ◽  
Time Frequency ◽  
Railway Tracks ◽  
Soil Foundation ◽  
The Impact

AbstractThe impact echo technique is one of the most useful non-destructive test methods for determining the thickness of concrete or detecting possible cracks or cavities in the internal parts of a concrete structure without damaging the surface. Many types of unstable conditions in railway tracks, including various modes of irregularities, may occur when cavities are generated directly under a concrete slab track or when a slight open space is made under a loose sleeper. In this study, we developed a nondestructive testing (NDT) system for detecting abnormalities in concrete tracks and performed 3D numerical simulations using the ABAQUS finite element analysis (FEA) program to investigate the impact echo response from a concrete track slab with different sizes of cavities. Sections of concrete slab were simulated as solid body masses under the railway tracks with gaps in the bodies themselves or with cavities existing between the track concrete layer (TCL) and the hydraulically stabilized base (HSB). We investigated the locations and depths of the cavities and gaps in the model concrete slab using the acoustic impact echo response based on the frequency response of the elastic waves generated in the slab. In addition, a Short-time Fourier Transform (STFT) and a wavelet technique were adopted for a time frequency analysis. Our study demonstrated that the impact echo technique developed in this study by FEA and NDT can measure and confirm the location and depth of cavities in concrete slabs.

scholarly journals Nonlinear residual echo suppression based on dual-stream DPRNN

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Hongsheng Chen ◽  
Guoliang Chen ◽  
Kai Chen ◽  
Jing Lu
Keyword(s):  
Adaptive Filters ◽  
Fine Tuning ◽  
Speech Communication ◽  
Time Frequency ◽  
Acoustic Echo ◽  
Processing Module ◽  
Auxiliary Signal ◽  
Echo Suppression ◽  
The Time Domain ◽  
The Impact

AbstractThe acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and the far-end signal. Usually, a post-processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of dual-path recurrent neural network (DPRNN) to improve the quality of speech communication. Both the residual signal and the auxiliary signal, the far-end signal or the output of the adaptive filter, obtained from the linear acoustic echo cancelation are adopted to form a dual-stream for the DPRNN. We validate the efficacy of the proposed method in the notoriously difficult double-talk situations and discuss the impact of different auxiliary signals on performance. We also compare the performance of the time domain and the time-frequency domain processing. Furthermore, we propose an efficient and applicable way to deploy our method to off-the-shelf loudspeakers by fine-tuning the pre-trained model with little recorded-echo data.

BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL

2016 ◽  
Vol 40 (5) ◽  
pp. 1019-1030
Author(s):  
Tao Liu ◽  
Xing Wu ◽  
Yu Guo ◽  
Chang Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Frequency Domain ◽  
Hidden Markov ◽  
Performance Degradation ◽  
Time Frequency ◽  
Rotating Machine ◽  
Accelerated Life ◽  
The Time Domain ◽  
Degradation Degree

Bearing is the key component in rotating machine. It is important to assess the performance degradation degree of bearings for making proactive maintenance and realizing near-zero downtime. A methodology based on orthogonal local preserving projection (OLPP) and continuous hidden Markov model (CHMM) is introduced in bearing performance degradation assessment. Firstly, the time domain, frequency domain and time-frequency domain features are extracted from the vibration signals. Then, the multi-dimensional features are reduced by OLPP. And the selection of the adjacent paragraph parameters in OLPP is optimized adaptively by minimizing the ratio of between-class distance to within-class distance. A CHMM is trained by using the reduced feature in normal condition. At last, the test bearing data are input into the pre-trained CHMM to calculate the log-likelihood of the test data, which can assess the performance degradation of bearings quantitatively. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method.

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

Sign in / Sign up

Export Citation Format

Share Document