Denoising ultrasonic pulse-echo signal using two-dimensional analytic wavelet thresholding

When an ultrasonic angle-beam pulse-echo setup is used, two kinds of noise are present in the received signal: (1) wedge noise, and (2) random noise. In this study, we propose a method for removing both random and wedge noises using a two-dimensional stationary wavelet transform (2D SWT). To improve the performance of the 2D SWT, we employ analytic wavelet thresholding. This requires calculating the analytic signal for a 2D signal and extracting the envelope of the signal, but no regular generalization of analytic signals to multi dimensions is available. We prove that under some certain conditions, the envelope of the ultrasonic B-scan can be extracted exactly by introducing a reference vector. The performance of the proposed denoising method is assessed using simulated and experimental data. The experimental results show that our proposed denoising method is able to reveal the echoes from a surface breaking crack as small as 0.1 mm.     

Sparsity enhancement for blind deconvolution of ultrasonic signals in nondestructive testing application

The received signal in ultrasonic pulse-echo inspection can be modeled as a convolution between an impulse response and the reflection sequence, which is the impulse characteristic of the inspected object. Deconvolution aims at approximately inverting this process to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small. This paper presents a modified minimum entropy blind deconvolution algorithm for deconvolving ultrasonic signals. Enhancement of the resolution is achieved by using the presented method. In addition, the presented approach will, in many cases, lead to a faster computation. A nonlinear function is the key point to the efficiency of the modified blind deconvolution algorithm, which is used to increase the sparsity of the iteration output and to decrease the influence of the added noise by replacing each iteration output by output of the nonlinear function. Simulations showed the efficiency of the modification as compared with minimum entropy deconvolution when deconvolving synthetic ultrasonic signals. Experimental results using real ultrasonic data evaluated further that the exact solution consistently yields good performance. The thickness of a thin steel sample can be calculated by the modified blind deconvolution filter with a reasonable accuracy.     

An adaptive Morlet wavelet filter for time-of-flight estimation in ultrasonic damage assessment

Ultrasonic non-destructive testing has been widely used in assessing the integrity of engineering materials such as high-temperature alloys and structures such as pipelines, bridges, and other load-bearing structures. The ultrasonic signals received from these structures are often noisy. Effective noise-reduction techniques are needed in order to accurately assess their condition. This paper presents a new digital signal processing method for estimating ultrasonic time-of-flight diffraction (TOFD). This method is based on wavelet analysis using the Morlet wavelet and the least mean squares (LMS) adaptive filter. The adaptive line enhancer (ALE) structure is used for the adaptive filter. The filter is designed to remove noise and identify the point at which the ultrasonic signal starts to reflect an echo from the tip of a crack. Both simulated and experimental data obtained from a steel plate with a crack produced by electrical-discharge-machining (EDM) are used to demonstrate the performance of the proposed method. This method is especially useful when the properties of the crack signal are unknown and the signal-to-noise ratio is low.     

Estimating ultrasonic time-of-flight through echo signal envelope and modified Gauss Newton method

The accurate estimation of ultrasonic Time-Of-Flight (TOF) is essential in ultrasonic non-destructive testing (NDT). In this paper, a novel method for TOF estimation through envelope is proposed. Firstly, the wavelet denoising technique is applied to the noisy echo to improve the estimation accuracy. Then, the Hilbert transform (HT) is used in ultrasonic signal processing in order to extract the envelope of the echo and to reduce the computational burden. Finally, the echo parameters are estimated by using a Modified Gauss Newton (MGN) based nonlinear Least Squares (LS) estimation method. And, a fast algorithm is adopted to estimate the parameters of M-superimposed echoes. Numerical simulation and experimental results have been carried out to show the performances of the proposed method in estimating TOF of ultrasonic signal.     

Estimating ultrasonic time of flight using envelope and quasi maximum likelihood method for damage detection and assessment

Time of flight (TOF) is widely used to locate and size faults in ultrasonic non-destructive evaluation (NDE). In this paper, we propose a novel model-based method for ultrasonic echo parameter estimation. The ultrasonic signal is assumed to be composed of an unknown number of Gaussian echoes corrupted by white Gaussian noise. Firstly, the Hilbert transform is used to extract the envelope of the signal. It is shown that the parameter estimation of the signal is improved by using the envelope. To estimate the parameters of the envelope of the signal, quasi maximum likelihood method is used. The number of echoes is estimated using consistent Akaike information criterion. Two measures are used to evaluate the performance of the proposed method: (a) probability of detection of backscattered echoes and (b) the error of estimated time of flights. The proposed method is then compared to the cross-correlation method and the maximum likelihood method which uses the original signal. Simulated and experimental signals are used to evaluate the performance of each method. Both experimental and simulated results show that the proposed method can improve the parameter estimation which ultimately enhances the damage detection and assessment.     

A hybrid signal pre-processing approach in processing ultrasonic signals with noise

Ultrasonic techniques have the potential to be used to detect sub-surface defects in aluminium castings. However, ultrasonic sensing techniques have not been successfully used to detect sub-surface defects in aluminium die castings with rough surfaces or in the ‘as-cast’ state due to the poor quality of signals. Ultrasonic signal noise caused by rough surfaces and grain size variations of the castings is difficult to eliminate. Hence, there is a need to process noisy ultrasonic signals to identify defects within the rough surface castings. This paper documents an investigation of ultrasonic signal analysis using artificial neural networks and hybrid signal pre-processing approaches for the purpose of detecting defects from noisy ultrasonic signals. In this investigation, ultrasonic signals were obtained from aluminium castings with different levels of surface roughness. The signals were first pre-processed using hybrid signal analysis techniques and then classified using an artificial neural network classifier. The hybrid pre-processing techniques utilised various combinations of fast Fourier transform (FFT), wavelet transform (WT) and principal component analysis. The best signal classification performance was generally achieved with a hybrid WT/FFT signal pre-processing technique.     

Maximum kurtosis principle for the parameter selection of Gabor wavelet and its application to ultrasonic signal processing

The noise suppression techniques with wavelet transform (WT) are widely used in nondestructive testing and evaluation (NDT&E), especially in ultrasonics. But the wavelet based filter has the property of equal Q-factor, so, it is impossible to choose the central frequency and the bandwidth arbitrarily at the same time. This paper develops a new technique using WT to eliminate this drawback. In this paper, a weak ultrasonic signals identification method by using the optimal parameter Gabor wavelet transform is proposed. We address the choice of the optimal central frequency and bandwidth of the Gabor wavelet using the kurtosis maximization algorithm. The central frequency and bandwidth of the optimal parameter Gabor wavelet matched that of the ultrasonic signal very well. Numerical and experimental results have been presented to evaluate the effectiveness of the optimal parameter Gabor wavelet transform on ultrasonic flaw detection. This technique is a simpler and effective technique for processing heavy noised ultrasonic signals.     

超声探测弱信号提取方法

为了在超声检测中排除噪声干扰，从强噪声背景中提取弱回波信号，利用小波变换技术从超声探测信号中提取弱缺陷回波信号，建立了超声缺陷回波的数学模型，并进行了仿真实验，其特有的“变焦距”特性使得小波分析在时域和频域中具有良好的分析能力。结果表明，利用小波变换方法能够很好地抑制噪声，提高信噪比，提取强噪声背景中的弱回波信号，且该方法原理简单，易于实现，在工程上有较高的应用价值。     

Matching-pursuit-based adaptive wavelet-packet atomic decomposition applied in ultrasonic inspection

A novel method of time-frequency analysis, wavelet-packet atomic decomposition based on the matching-pursuit (MP) algorithm, is proposed for improvement of ultrasonic flaw detection during ultrasonic NDT. The MP algorithm is used to decompose a nonlinear and nonstationary signal into given atoms in an overcomplete wavelet-packet dictionary. The wavelet-packet dictionary with Daubechies wavelet functions, which well match the observed ultrasonic flaw echoes, is selected. By means of adaptively selecting the optimum atoms to reconstruct a signal, it is possible to obtain spare approximations of the original signal with less complexity and to efficiently improve a signal corrupted by noise. A distinct SNR enhancement for an ultrasonic echo during flaw detection is verified by computer simulation and experimental results. The text was submitted by the authors in English.     

Sign least mean squares-based deconvolution technique for ultrasonic testing

Sign LMS algorithms, members of the simplified adaptive least-mean-squares class, have been developed to reduce computational complexity and simplify hardware implementation. These advantages make them suitable to utilize in ultrasonic testing units, a class of applications requiring simple and efficient signal processing algorithms. This paper proposes a specific sign LMS adaptive filters-based deconvolution technique for ultrasonic straight beam pulse-echo inspections. It extracts only two of the interface echoes multiple reflections for enhanced resolution and quality-enriched presentation; this technique is named “selective deconvolution”. Resolution enhancement and presentation’s quality enrichment performance among the different sign LMS algorithms were investigated by experiments, and based on performance, the methods themselves were compared. Computational requirements are also presented. The proposed technique with the various adaptive sign LMS filters gave satisfactory results.     

A New ultrasonic signal processing scheme for detecting echoes of different spectral characteristics in concrete using empirical mode decomposition

In this work, a new signal processing scheme is proposed to improve ultrasonic echoes detection in concrete, a construction material and inhomogeneous propagation medium, in which the backscattering and the grain noise mask the useful signals. Firstly, the time-domain ultrasonic signal is measured. Then the original signal is separated into Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition. Secondly, the Fourier analysis is performed on each IMF. Examining the behaviors of the IMFs in the time and frequency domain, one can judge which IMF represents noise, echoes wave. Then we use nonlinear processing techniques to recombine the selected IMFs. Experimental results are presented and analyzed on a trapezoidal concrete/mortar/cement-paste prism specimen, showing that the proposed technique has an excellent performance. The improvement in defect detection was experimentally verified too on a prism based specimen of mortar with artificial defect.     

High-accuracy ultrasonic temperature measurement based on MLS-modulated continuous wave

Ultrasonic temperature measurement has the potential to improve measurement accuracy by increasing the length of a received signal due to its excellent performance with noise resistance. However, when the distance between the transmitter and receiver is limited, the received signal can be polluted by strong multiple echoes, which can significantly degrade temperature accuracy. This paper proposes a method for high-resolution ultrasonic temperature measurement. With the use of a maximum length sequence (MLS)-modulated continuous wave, the obstructive effect of echoes is effectively suppressed. A hybrid method is employed for accurate time-of-flight (TOF) estimation by incorporating both cross-correlation and phase shift (PS), which is the basis of highly accurate temperature measurement. The experimental results in distilled water show that the proposed method estimates TOF with a standard deviation of less than 0.3 ns, and temperature errors consistently remain within ±0.04 °C.     

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

空气耦合超声信号的小波阈值滤噪试验研究

穿透式空气耦合超声检测中,由于较低声波透射率、激励接收系统噪声及声波在介质中的散射噪声导致接收信号信噪比较低,小波阈值滤噪技术在解决上述问题时面临小波基、分解层数及阈值函数的选取难题。基于小波分析的基本原理,以单因素分析方法开展小波阈值滤噪试验研究。选择不同小波族(Daubechies,Symlet和Coiflet)中的小波基、小波分解层数(4～8层)及阈值函数(软阈值及改进阈值函数)对实际含噪超声信号进行小波阈值滤噪处理,并通过对比滤噪信号的信噪比及频谱特性得出不同参数对滤噪效果的影响。结果表明,选择Coiflet小波族中的小波基能获得具有更高信噪比及透射信号幅值的滤噪信号;分解层数越高,滤噪信号的信噪比越高,但增长趋势渐趋稳定;阈值函数对滤噪性能的影响并不十分显著,一般采用软阈值函数或改进阈值函数就能获得良好滤噪效果。     

Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise

In practical engineering applications, useful information is often submerged in strong noise and the feature information is difficult to be extracted. Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed. Firstly, noisy signal is processed by orthogonal wavelet transform to decompose the signal into multi-scale ingredients. According to the orthogonal wavelet transform coefficients characteristics of 1/f distribution, multi-scale noise is constructed so as to make the frequency-band containing the driving frequency be enhanced through SR system. Thus multi-frequency weak signal is detected. The method is effective to detect multi-frequency weak signal under colored noise background. Experiment signal analysis results show that the proposed method is simple for multi-frequency weak signal detection, and has good prospects for engineering applications.     

Ultrasonic flaw detection during NDE of oil pipelines via a resonance filter

Ultrasonic nondestructive evaluation (NDE) is widely used for detecting flaws in oil pipelines. The backscattering noise may attain peak values greater than the sought flaw pulse. For improving the signal-to-noise ratio (SNR) and extracting a flaw echo corrupted by noise, a resonance demodulation technique and a resonance filter are proposed. The impulse response of the resonance filter can be represented as a superposition of Gaussian echoes. The analysis results show that this approach is more effective in extracting the information of a small flaw during pipeline NDE. The amplitude envelope of the processed ultrasonic signal after envelope detection obtains a notable SNR enhancement. The text was submitted by the authors in English.     

HHT在Lamb波检测信号分析中的应用

将一种新的超声信号处理技术用于Lamb波波形中多个模式到达时间的提取。通过将希尔伯特-黄变换(Hilbert-Huang transform,简称HHT)与快速傅里叶变换(fast Fourier transform,简称FFT)、小波变换(wavelettransform,简称WT)在时频分辨率方面的比较,表明HHT能够精确识别信号中两种频率分量突变的时刻,显示了HHT方法的优越性。将HHT方法的特性用于Lamb波模式到达时间的提取,从HHT的能量-时间图上可以看出,能量峰值时刻对应着各Lamb波模式的到达时间。试验结果与理论值具有较好的一致性。     

基于Laguerre滤波的超声波流量计降噪方法研究

针对气体超声波流量计利用过零检测定位波形脉冲时的干扰因素,以及计算系统对数据处理实时性的要求,对基于Laguerre滤波的递归最小二乘（RLS）降噪算法进行了研究,实现了在处理速度与稳定性上的折中。为信号后处理提供了理想的波形,避免了波形失真导致脉冲漏检、零位判断错误等误差因素的产生,给出了实时性强、波形定位准确、系统稳定性和抗干扰性能良好的气体超声流量计信号处理算法。研究结果表明,基于此算法的气体超声波流量计经检验具备2级精度。     

Noise cancellation of ultrasonic NDE signals using blind source separation

This paper proposes a new denoising method for ultrasonic NDE (nondestructive evaluation) signals using blind separation (BSS) technology. The proposed denoising method consists of four steps. First, a reconstructed phase space (RPS) is constructed from observed ultrasonic NDE signals. The information about the underlying sources (e.g., ultrasonic signal, noise, etc.) acting on this system is contained in this RPS. Second, independent component analysis (ICA) is performed on the RPS to recover all sources underlying the RPS. Next, the ultrasonic signal component is selected by a decision criterion related to the denoising application and, finally, is reconstructed to obtain the denoised ultrasonic signal. To validate the proposed method, it has been applied to the experimental ultrasonic NDE signals of the test sample and is compared with the wavelet denoising method in SNR (signal-to-noise ratio) enhancement. The experimental results show that the SNR of the ultrasonic NDE signals can be enhanced greatly using the proposed denoising method and the proposed method has almost the same denoising performance as the wavelet denoising method in SNR enhancement. A trait of the proposed denoising method is the ability to denoise ultrasonic NDE signals by separating the ultrasonic signal and noise using blind source separation technology. The text was submitted by the authors in English.