An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

Chang, Yong; Ye, Zi; Zhao, Jiyuan; Yang, Zhe; He, Wangpeng; Sun, Hailiang

doi:10.1088/1361-6501/aa52ae

Cited by 46 publications

(34 citation statements)

References 34 publications

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“…In this paper, the split augmented Lagrangian shrinkage (SALSA) [55] method is used to solve the nonconvex WATV denoising problem. The good convergence of SALSA has been proven in practice [42], [55]. With variable splitting, problem (10) can be transformed into the constrained optimization problem min a,c…”

Section: Salsa Algorithm For Nonconvex Watvmentioning

confidence: 99%

“…In this case, the penalty function is defined to measure the number of nonzero values in x, i.e., φ (x) = x 0 where x 0 is the l 0 -norm and defined as x 0 = N n=1 |x n | 0 . Unfortunately, with φ (x) defined as such, the regularization problem in (5) is an NP-hard problem for which the objective function F (x) cannot readily to minimize [42], [43]. Therefore, it is common to replace the l 0 -norm by the l 1 -norm as the penalty function in practical applications, because it induces sparsity most effectively and does not sacrifice the convexity of the objective function.…”

Section: B Total Variation Denoisingmentioning

confidence: 99%

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Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Jiang¹,

Chang

Cong³

et al. 2020

IEEE Access

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The vibration-based analysis is an effective technique for planetary gearbox fault diagnosis, but a difficult task is how to accurately identify fault features from noisy vibration signals. In this paper, a nonconvex wavelet thresholding total variation (WATV) denoising method is proposed for planetary gearbox fault diagnosis, which combines wavelet-domain sparsity and total variation (TV) regularization. The TV regularization algorithm is employed to modify the retained wavelet coefficients so that the occurrence of oscillations caused by wavelet thresholding is suppressed. To overcome the underestimation shortcoming of L1-norm regularization, nonconvex penalty function regularization is used to strongly promote the sparsity of estimation while guaranteeing that the global optimal solutions are obtained even though the objective function is nonconvex. Then, the split augmented Lagrangian shrinkage (SALSA) method is developed to solve the nonconvex WATV denoising problem. Two experimental studies are performed to verify the performance and effectiveness of the proposed method. Comparisons with the soft thresholding and basis pursuit denoising (BPD) methods show that the proposed method can accurately estimate the fault features from vibration signals, which means that the proposed method is an effective and promising tool for planetary gearbox fault diagnosis.

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Section: Salsa Algorithm For Nonconvex Watvmentioning

confidence: 99%

Section: B Total Variation Denoisingmentioning

confidence: 99%

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Jiang¹,

Chang

Cong³

et al. 2020

IEEE Access

Self Cite

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“…Existing techniques for crack detection rely on the visual analysis of the analysed segment [10], the implementation of eddy current [11], in the case of metallic structures, or ultrasonic techniques [12]. Chen et al [13] propose a fusion between a convolutional neural network and a Naive Bayes to analyse video frames for crack detection in nuclear reactors.…”

Section: Introductionmentioning

confidence: 99%

Implementing Tactile and Proximity Sensing for Crack Detection

Palermo

Konstantinova

Althoefer

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Remote characterisation of the environment during physical robot-environment interaction is an important task commonly accomplished in telerobotics. This paper demonstrates how tactile and proximity sensing can be efficiently used to perform automatic crack detection. A custom-designed integrated tactile and proximity sensor is implemented. It measures the deformation of its body when interacting with the physical environment and distance to the environment's objects with the help of fibre optics. This sensor was used to slide across different surfaces and the data recorded during the experiments was used to detect and classify cracks, bumps and undulations. The proposed method uses machine learning techniques (mean absolute value as feature and random forest as classifier) to detect cracks and determine their width. An average crack detection accuracy of 86.46% and width classification accuracy of 57.30% is achieved. Kruskal-Wallis results (p<0.001) indicate statistically significant differences among results obtained when analysing only force data, only proximity data and both force and proximity data. In contrast to previous techniques, which mainly rely on visual modality, the proposed approach based on optical fibres is suitable for operation in extreme environments, such as nuclear facilities in which nuclear radiation may damage the electronic components of video cameras.

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“…CS had the potential as the alternative to data compression and it had been investigated for accelerometer signals and structural response signals [19]- [21]. Because guided wave could be modeled as the convolution of incident signal and reflection sequence, an adaptive sparse deconvolution method was proposed to distinguish overlapped wavepackets [22]. However, the recovery or reconstruction attracts more attention in guided wave field.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing Method for Health Monitoring of Pipelines Based on Guided Wave Inspection

Wang

Huang

Wang

et al. 2020

IEEE Trans. Instrum. Meas.

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The pipeline in-service needs to be inspected in certain period to master its structural health status. Ultrasonic guided wave which can propagate along pipelines with less energy loss provides an efficient method for long-term in situ inspection. Guided wave can detect both corrosion and cracks existing in structures. To overcome the problem of huge amounts of data and to maintain defect identification accuracy, the compressed sensing method for guided wave inspection is proposed. The compression process is essentially a scheme of analog to information conversion to compress the signal. It is accomplished by random demodulation and the equivalent sampling rate below the Nyquist rate helps to save most of storage. The compressed data is recovered to the sparse spatial domain based on the constructed dictionary from a guided wave propagation model. To verify the effectiveness of proposed method, both numerical simulations and experimental investigations are conducted. The results indicate the availability of compression and high accuracy of defect location after recovery. The influences of different compression schemes and compression ratios are further analyzed. In addition, the comparisons with direct recovery without compression and traditional analysis methods demonstrate the advantageous performance of the proposed method.

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An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

Cited by 46 publications

References 34 publications

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Implementing Tactile and Proximity Sensing for Crack Detection

Compressed Sensing Method for Health Monitoring of Pipelines Based on Guided Wave Inspection

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