Fast neutron and γ-ray backscatter radiography for the characterization of corrosion-born defects in oil pipelines

Licata, M.; Parker, Helen M. O'd.; Aspinall, Michael; Bandala, Manuel; Cave, Frank; Conway, Sebastian; Gerta, Domas; Joyce, Malcolm J.

doi:10.1051/epjconf/202022506009

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…Traditional non-destructive pipeline corrosion testing methods include radiographic inspection, magnetic flux leakage detection, eddy current detection and piezoelectric sensing [6][7][8]. Radiographic detection method uses radioactive elements to irradiate the pipeline, and the internal situation of the pipeline is observed based on the attenuation of the ray reflects [9]. However, this method requires complex operations to ensure safe operation.…”

Section: Introductionmentioning

confidence: 99%

A pipeline corrosion detecting method using percussion and residual neural network

Yang,

Ji,

Wang

et al. 2024

Meas. Sci. Technol.

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Corrosion of pipeline walls can lead to serious safety accidents such as leaks, fires and even explosions. This paper proposes a corrosion detection method using deep learning based on percussion sound for pipelines. The percussion induced acoustic signals are processed by wavelet threshold noise reduction and double threshold endpoint detection to generate the Mel spectrograms, and then an 18-layer Residual Network (ResNet18) is used to mine the depth information and classify the degree of pipeline corrosion. We conducted experiments to verify the validity of the approach. Seven working conditions are generated by electrochemical corrosion of a pipe specimen, and percussions are applied at five different positions under the same working conditions to collect the impact acoustic signals. The test results show that the method can quickly, efficiently and accurately detect the degree of pipeline corrosion, classify the degree of pipe corrosion without being affected by the striking position Therefore, the model has great potential for application in detecting the internal corrosion of pipelines based on percussion sounds.

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Section: Introductionmentioning

confidence: 99%

A pipeline corrosion detecting method using percussion and residual neural network

Yang,

Ji,

Wang

et al. 2024

Meas. Sci. Technol.

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“…Traditional non-destructive testing methods (NDT) for pipeline corrosion include visual inspection, magnetic flux leakage detection, eddy current detection, ultrasonic tomography, and X-ray technology [ 8 , 9 , 10 , 11 , 12 ]. Visual inspection relies on the expertise of inspectors, and thus, its reliability of results is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Yang

Zhang

Zhou³

et al. 2023

Sensors

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In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.

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“…Currently, several common non-destructive testing (NDT) methods have been attempted to detect damage of certain structures, including the acoustic emission (AE) method [ 5 , 6 ], the ultrasonic method [ 7 , 8 ], the radiography method [ 9 ], etc. AE is the phenomenon of transient elastic waves generated by the rapid release of local energy within a material (or structure).…”

Section: Introductionmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time–frequency representations in the form of mel spectrograms. Then, two image descriptors—the local binary pattern (LBP) and histogram of oriented gradient (HOG)—are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

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Fast neutron and γ-ray backscatter radiography for the characterization of corrosion-born defects in oil pipelines

Cited by 6 publications

References 5 publications

A pipeline corrosion detecting method using percussion and residual neural network

A pipeline corrosion detecting method using percussion and residual neural network

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

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