Eddy Current Detecting of Leak Hole in Pipeline by Wavelet Packet Signal Analysis Method

2013 ◽  
Vol 291-294 ◽  
pp. 2486-2491
Author(s):  
Yue Shen ◽  
Ling Tan Zhang
Keyword(s):  
Eddy Current ◽  
Steel Plate ◽  
Multiple Scales ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Measuring Method ◽  
Transient Signal ◽  
Measured Signal ◽  
Accurate Identification ◽  
Time Frequency

In the process of detecting leak hole in pipeline by intelligent pig with eddy current measuring method, the cut off effect of eddy current field in the closed cross section of leak hole in steel plate is weak due to geometric deformation of eddy current on the edge of the hole produced by destroyed drilling. As a result, the measured signal is very unobvious. Meanwhile, owing to the rough surface of steel plate, the periodic interference generated by movement of the detecting probe cannot be eliminated or be inhibited easily by conventional signal processing methods for its large amplitude and approximate frequency band with the leak hole signals, the signal to noise ratio (SNR) of measured signal is very low and the accurate identification of leak hole cannot be guaranteed. The wavelet transform, with characteristics of time-frequency localization and multiple scales, is a useful and effective method for identifying singularity of the signals and adapts to detect the transient signal or extract non-stationary information in the signals with strong periodic interference and noise. The reconstructing signal SNR will be increased greatly in eddy current detecting of leak hole in the pipeline with wavelet packet analysis of the signal by constructing the self-defined cost function based on maximum SNR to obtain optimal wavelet packet basis function. This ensures good detection and location of leak hole in the pipeline.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

scholarly journals On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 269
Author(s):  
Ismail Mohamed ◽  
Yaser Dalveren ◽  
Ferhat Ozgur Catak ◽  
Ali Kara
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Criterion ◽  
Transient Signal ◽  
Detection Accuracy ◽  
Large Set ◽  
Accurate Identification ◽  
Transient Region ◽  
Starting Point ◽  
Wide Range ◽  
Criterion Method

In the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC-a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set of Wi-Fi signals captured from various Wi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signal-to-noise ratio (SNR) values defined as low (−3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.

Design of orthogonal eddy-current probe for weld crack detection of carbon-steel plate

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1347-1355
Author(s):  
Tao Chen ◽  
Xiaoqi Xiao ◽  
Lihong Zhang ◽  
Cheng Lv ◽  
Zhiyang Deng ◽  
...  
Keyword(s):  
Carbon Steel ◽  
Eddy Current ◽  
Crack Detection ◽  
Steel Plate ◽  
Signal To Noise Ratio ◽  
Detection Sensitivity ◽  
Scanning Angle ◽  
Length Width ◽  
Lift Off ◽  
Detection Effect

Due to uneven surface and lift-off effect, it is difficult to detect weld crack by eddy-current testing. A new orthogonal eddy-current probe for weld crack detection of carbon-steel plate was designed in this paper. Based on COMSOL Multiphysics, the influence of scanning angle on detection sensitivity of the probe was compared firstly. Then, the effects of coil width, coil side length, detection coil height, and lift-off distance on detection sensitivity of the probe were studied, respectively. Finally, the test piece of carbon-steel plate weld with crack, and the physical probe used to verify the crack detection effect were made. The experimental results show that the weld crack of carbon-steel plate with length × width × depth of 20.0 mm × 0.3 mm × 1 mm can be effectively identified, and the lift-off noise can be effectively suppressed by the method presented in this paper. At the same time, the signal-to-noise ratio of the probe keeps constant in the lift-off distance range of 0.3 mm–3.0 mm.

Feature Extraction From Energy Distribution of Stamping Processes Using Wavelet Transform

2002 ◽  
Vol 8 (7) ◽  
pp. 1023-1032 ◽  
Author(s):  
M. Ge ◽  
G. C. Zhang ◽  
R. Du ◽  
Y. Xu
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Transient Signal ◽  
Energy Distributions ◽  
Time Frequency ◽  
Stamping Process ◽  
The Press ◽  
On Line ◽  
Rich Information ◽  
Frequency Band Energy

As one of the most commonly used manufacturing processes, stamping operations are applied to an extended range from centimeter-class parts to meter-class parts. The demand for the quality and productivity of stamping products is ever increasing. Hence, the extraction of the appropriate feature to implement on-line monitoring has been attempted. The vibration signals provide rich information for finding the dynamic behavior at high frequency vibration of the press. In this paper, an accelerometer has been employed in place of tonnage sensors or strain gauges to monitor the stamping process. In order to characterize the transient signal of the process, this work extracts the energy densities and frequency band energy (FBE) from the vibration signal. Owing to its inherent properties, the wavelet packet transformation decomposes the original signal into basis functions, with different energy distributions dominating in different time-frequency bands. Based on the experimental results, this suggests that extracting FBE from a vibration signal using a wavelet packet as a feature for fault diagnosis can, in practice be an effective approach for stamping process monitoring.

scholarly journals Research on Detection Mechanism of Weld Defects of Carbon Steel Plate Based on Orthogonal Axial Eddy Current Probe

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5515
Author(s):  
Linnan Huang ◽  
Chunhui Liao ◽  
Xiaochun Song ◽  
Tao Chen ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Carbon Steel ◽  
Eddy Current ◽  
Steel Plate ◽  
Difficult Problem ◽  
Eddy Current Testing ◽  
Detection Sensitivity ◽  
Detection Mechanism ◽  
Current Testing ◽  
Weld Defects ◽  
Lift Off

The uneven surface of the weld seam makes eddy current testing more susceptible to the lift-off effect of the probe. Therefore, the defect of carbon steel plate welds has always been a difficult problem in eddy current testing. This study aimed to design a new type of eddy current orthogonal axial probe and establish the finite element simulation model of the probe. The effect of the probe structure, coil turns, and coil size on the detection sensitivity was simulated. Further, a designed orthogonal axial probe was used to conduct a systematic experiment on the weld of carbon steel specimens, and the 0.2 mm width and 1 mm depth of weld defects of carbon steel plates were effectively detected. The experimental results showed that the new orthogonal axial eddy current probe effectively suppressed the unevenness effect of the weld surface on the lift-off effect during the detection process.

scholarly journals New Time-Frequency Transient Features for Nonintrusive Load Monitoring

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1437
Author(s):  
Mahfoud Drouaz ◽  
Bruno Colicchio ◽  
Ali Moukadem ◽  
Alain Dieterlen ◽  
Djafar Ould-Abdeslam
Keyword(s):  
Transient Signal ◽  
Learning Tools ◽  
Time Frequency ◽  
Energy Measure ◽  
Load Monitoring ◽  
Feature Based ◽  
Stockwell Transform ◽  
Processing Techniques ◽  
Equipment Laboratory ◽  
New Time

A crucial step in nonintrusive load monitoring (NILM) is feature extraction, which consists of signal processing techniques to extract features from voltage and current signals. This paper presents a new time-frequency feature based on Stockwell transform. The extracted features aim to describe the shape of the current transient signal by applying an energy measure on the fundamental and the harmonic frequency voices. In order to validate the proposed methodology, classical machine learning tools are applied (k-NN and decision tree classifiers) on two existing datasets (Controlled On/Off Loads Library (COOLL) and Home Equipment Laboratory Dataset (HELD1)). The classification rates achieved are clearly higher than that for other related studies in the literature, with 99.52% and 96.92% classification rates for the COOLL and HELD1 datasets, respectively.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

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