Wavelet Analysis of Sound Signal in Fluid-Filled Viscoelastic Pipes

2002 ◽  
Author(s):  
Matjaz Prek
Keyword(s):  
Wavelet Transform ◽  
Material Properties ◽  
Sound Speed ◽  
Pipe Wall ◽  
Sound Signal ◽  
Wall Material ◽  
Flexural Waves ◽  
Frequency Dependent ◽  
Time Frequency ◽  
The Impact

In viscoelastic pipes, where the material properties depends on a complex bulk modulus as well as on a complex shear modulus, the sound field within the fluid is affected. Therefore, the dispersion of flexural waves occurs in the pipe, while the speed of flexural waves decreases due to the coupled fluid mass. Coupling between the pipe wall and the fluid also decreases the sound speed in the fluid. Likewise, the speed of sound in fluid is frequency-dependent, just as the group velocity of bending waves depends on the frequency. Wavelet transform of non-stationary sound signal was used to identify the frequency-dependent fluid sound speed. A time-frequency map, constructed by plotting the wavelet coefficient against the translation and scale parameters, shows an alteration in the low frequency waves. The so called fluid mode and pipe mode resonant frequencies are also clearly evident. Lastly, the impact of different pipe wall material properties is also shown. Results suggests that the wavelet transform gives in general more information from measured results.

Wavelet Transform of Sound Signal in Fluid-Filled Viscoelastic Pipes

2001 ◽  
Author(s):  
Matjaz Prek
Keyword(s):  
Wavelet Transform ◽  
Material Properties ◽  
Sound Speed ◽  
Pipe Wall ◽  
Sound Signal ◽  
Wall Material ◽  
Flexural Waves ◽  
Frequency Dependent ◽  
Time Frequency ◽  
The Impact

Abstract In viscoelastic pipes, where the material properties depends on a complex bulk modulus as well as on a complex shear modulus, the sound field within the fluid is affected. Therefore, the dispersion of flexural waves occurs in the pipe, while the speed of flexural waves decreases due to the coupled fluid mass. Coupling between the pipe wall and the fluid as decreases the sound speed in the fluid. Likewise, the speed of sound in fluid is frequency-dependent, just as the group velocity of bending waves depends on the frequency. Wavelet transform of non-stationary sound signal was used to identify the frequency-dependent fluid sound speed. A time-frequency map, constructed by plotting the wavelet coefficient against the translation and scale parameters, shows an alteration in the low frequency waves. The so called “fluid mode” and “pipe mode” resonant frequencies are also clearly evident. Lastly, the impact of different pipe wall material properties is also shown. Wavelet analysis of the measured impulse response of a fluid-filled viscoelastic pipe provides useful tool for investigating its acoustical properties.

Impact Localization on a Composite Plate With Unknown Material Properties Using PWAS Transducers and Wavelet Transform

2017 ◽  
Author(s):  
Asaad Migot ◽  
Victor Giurgiutiu
Keyword(s):  
Wavelet Transform ◽  
Nonlinear Equations ◽  
Material Properties ◽  
Composite Plate ◽  
Linear Equations ◽  
Active Sensors ◽  
Piezoelectric Wafer Active Sensors ◽  
Piezoelectric Wafer ◽  
The Impact ◽  
Impact Localization

In this work, an impact experiment on a composite plate with unknown material properties (its group velocity profile is unknown) is implemented to localize the impact points. A pencil lead break is used to generate acoustic emission (AE) signals which are acquired by six piezoelectric wafer active sensors (PWAS). These sensors are distributed with a particular configuration in two clusters on the plate. The time of flight (TOF) of acquired signals is estimated at the starting points of these signals. The continuous wavelet transform (CWT) of received signals are calculated with AGU Vallen wavelet program to get the accurate values of the TOF of these signals. Two methods are used for determining the coordinates of impact points (localization the impact point). The first method is the new technique (method 1) by Kundu. This technique has two linear equations with two unknowns (the coordinate of AE source point). The second method is the nonlinear algorithm (method 2). This algorithm has a set of six nonlinear equations with five unknowns. Two MATLAB codes are implemented separately to solve the linear and nonlinear equations. The results show good indications for the location of impact points in both methods. The location errors of calculated impact points are divided by constant distance to get independent percentage errors with the site of the coordinate.

scholarly journals Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Junfeng Guo ◽  
Xingyu Liu ◽  
Shuangxue Li ◽  
Zhiming Wang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Time Frequency ◽  
The Impact

As one of the important parts of modern mechanical equipment, the accurate real-time diagnosis of rolling bearing is particularly important. Traditional fault diagnosis methods have some disadvantages, such as low diagnostic accuracy and difficult fault feature extraction. In this paper, a method combining Wavelet transform (WT) and Deformable Convolutional Neural Network (D-CNN) is proposed to realize accurate real-time fault diagnosis of end-to-end rolling bearing. The vibration signal of rolling bearing is taken as the monitoring target. Firstly, the Orthogonal Matching Pursuit (OMP) algorithm is used to remove the harmonic signal and retain the impact signal and noise. Secondly, the time-frequency map of the signal is obtained by time-frequency transform using Wavelet analysis. Finally, the D-CNN is used for feature extraction and classification. The experimental results show that the accuracy of the method can reach 99.9% under various fault modes, and it can accurately identify the fault of rolling bearing.

An investigation on early bearing fault diagnosis based on wavelet transform and sparse component analysis

2016 ◽  
Vol 16 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Hongyu Cui ◽  
Yuanying Qiao ◽  
Yumei Yin ◽  
Ming Hong
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Component Analysis ◽  
Time Frequency ◽  
Bearing Fault Diagnosis ◽  
Sparse Component Analysis ◽  
The Impact ◽  
Mode Mixing ◽  
Separation Functions

Rolling bearings, as important machinery components, strongly affect the operation of machines. Early bearing fault diagnosis methods commonly take time–frequency analysis as the fundamental basis, therein searching for characteristic fault frequencies based on bearing kinematics to identify fault locations. However, due to mode mixing, the characteristic frequencies are usually masked by normal frequencies and thus are difficult to extract. After time–frequency decomposition, the impact signal frequency can be distributed among multiple separation functions according to the mode mixing caused by the impact signal; therefore, it is possible to search for the shared frequency peak value in these separation functions to diagnose bearing faults. Using the wavelet transform, time–frequency analysis and blind source separation theory, this article presents a new method of determining shared frequencies, followed by identifying the faulty parts of bearings. Compared to fast independent component analysis, the sparse component analysis was better able to extract fault characteristics. The numerical simulation and the practical application test in this article obtained satisfactory results when combining the wavelet transform, intrinsic time-scale decomposition and linear clustering sparse component analysis, thereby proving the validity of this method.

scholarly journals Experimental Investigations of Offshore Sand Production Monitoring Based on the Analysis of Vibration in Response to Weak Shocks

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yichen Li ◽  
Gang Liu ◽  
Zongwen Jia ◽  
Min Qin ◽  
Gang Wang ◽  
...  
Keyword(s):  
Pipe Wall ◽  
Oil Wells ◽  
Impact Response ◽  
Sand Production ◽  
Time Frequency ◽  
Response Characteristics ◽  
Sand Particles ◽  
Production Monitoring ◽  
Offshore Oil ◽  
The Impact

Sand production is a problem that is often encountered in unconventional oil and gas exploitation and that is difficult to effectively solve. Accurate online monitoring of sand production is one of the keys to ensuring the safety and long-term production of oil wells as well as efficient production throughout the life cycle of production wells. This paper proposes a method for monitoring sand production in offshore oil wells that is based on the vibration response characteristics of sand-carrying fluid flow impinging on the pipe wall. This method uses acceleration sensors to obtain the weak vibration response characteristics of sand particles impinging on the pipe wall on a two-dimensional time-frequency plane. The time-frequency parameters are further optimized, and the ability to identify weakly excited vibration signals of sand particles in the fluid stream is enhanced. The difference between the impact response of the sand particles and the impact response of the fluid flow to the pipe wall is identified, and corresponding indoor verification experiments are carried out. Under different sand contents, particle sizes, and flow rates (sand content 0-2‰, sand particle size 96-212 μm, and flow velocity 1-3 m/s), the impact response frequency of sand particles to the pipe wall exhibits good consistency. The characteristic frequency band of sand impacting the pipe wall is 30-50 kHz. A statistical method is used to establish the response law of the noise signal of the fluid. Based on this knowledge, a real-time calculation model of sand production in offshore oil wells is constructed, and the effectiveness of this model is verified. Finally, a field test is carried out with a self-developed sand production signal dynamic time-frequency response software system on 4 wells of an oil production platform in the Bohai Sea. This system can effectively distinguish sand-producing wells from non-sand-producing wells. The dynamic time-frequency response, field test results, and actual laboratory results are consistent, verifying the effectiveness of the method proposed in this paper and further providing a theory for improving the effectiveness of the sand production monitoring method under complex multiphase flow conditions. This study also provides technical guidance for the industrial application of sand production monitoring devices in offshore oil wells.

scholarly journals Flexural Waves in Fluid-Filled Tubes Subject to Axial Impact

2008 ◽  
Author(s):  
Kazuaki Inaba ◽  
Joseph E. Shepherd
Keyword(s):  
Sound Speed ◽  
Wall Stress ◽  
Stress Waves ◽  
Thick Wall ◽  
Flexural Waves ◽  
Steel Tubes ◽  
Wave Speeds ◽  
Inner Diameter ◽  
Peak Pressures ◽  
The Impact

We experimentally studied the propagation of coupled fluid stress waves and tube flexural waves generated through projectile impact along the axis of a water-filled tube. We tested mild steel tubes, 38–40 mm inner diameter and wall thickness of 0.8, 6.4, and 12.7 mm. A steel impactor was accelerated using an air cannon and struck a polycarbonate buffer placed on the top water surface within the tube. Elastic flexural waves were observed for impact speeds of 5–10 m/s and plastic waves appeared for impact speeds approaching 20 m/s for a 0.8 mm thickness tube. We observed primary wave speeds of 1100 m/s in a 0.8 mm thickness tube, increasing to the water sound speed with 6.4 and 12.7 mm thickness tubes. Comparison of our measurements in the 0.8 mm thickness tube with Skalak’s water hammer theory indicates reasonable agreement between predicted and measured peak strains as a function of the impact buffer speed. For thick-wall tubes, the correlation between experimentally determined peak pressures and strains reveals the importance of corrections for the through-wall stress distribution.

scholarly journals Theoretical Prediction Method for Erosion Damage of Horizontal Pipe by Suspended Particles in Liquid–Solid Flows

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4099
Author(s):  
Guoqiang Liu ◽  
Wenzhe Zhang ◽  
Liang Zhang ◽  
Jiarui Cheng
Keyword(s):  
Small Angle ◽  
Pipe Wall ◽  
Low Cycle Fatigue ◽  
Prediction Method ◽  
Impact Angle ◽  
Wall Material ◽  
Two Phase ◽  
Horizontal Pipe ◽  
Erosion Damage ◽  
The Impact

In order to study the erosion of a pipe wall via a liquid–solid suspension flow, a two-phase flow model combined with an erosion forecasting model for multiparticle impact on horizontal pipe wall surfaces was established in this work on the basis of low-cycle fatigue theory. In the model establishment process, the effects of particle motion and material damage were considered, and a simplified method for predicting horizontal wall erosion was obtained. The calculated results showed that the particles impact the wall at a small angle of most liquid flow velocities, causing cutting erosion damage of the wall. The settling velocity and fluctuating velocity of the particles together determine the radial velocity of the particles, which affects the impact angle of the particles. The cutting erosion caused by the small-angle impact of the particles in the pipe is more likely to cause rapid loss of the wall material. Therefore, the pipe wall is usually evenly thinned.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency
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