Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Yang, Dan; Xiong, Mengzhou; Wang, Tao; Lu, Guangtao

doi:10.3390/app12042127

Cited by 10 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These neurons are connected to the output layer for classification [50]. Notably, the automatic feature extraction aspect of CNN models empowered looseness classification in bolted joints [34], damage detection of concrete structures [51], ponding detection in pipelines [52], etc.…”

Section: Cnns For Bolt Looseness Classificationmentioning

confidence: 99%

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Chelimilla,

Chinthapenta,

Korla

2024

Smart Mater. Struct.

View full text Add to dashboard Cite

Deep learning models such as convolutional neural networks (CNNs) encounter challenges, including instability and overfitting, while predicting bolt looseness in data-scarce scenarios. In this study, we proposed a novel audio signal augmentation approach to classify bolt looseness in the event of data deficiency using CNN models. Audio signals at varied bolt torque conditions were extracted using the percussion method. Audio signal augmentation was performed using signal shifting and scaling strategies after segmenting the extracted audio signals. The unaugmented and augmented audio signals were transformed into scalograms using the continuous wavelet transform approach to train the CNN models. Upon training with augmented datasets, a promising improvement in the loss and accuracy of the CNN models in recognizing bolt looseness was noticed. One of the significant observations from the current study is that the implementation of audio signal augmentation improved the extrinsic generalization ability of the CNN models to classify bolt looseness. A maximum increase of 73.5% to identify bolt looseness in novel data was exhibited as compared to without augmentation. Overall, a maximum accuracy of 94.5% to classify bolt looseness in unseen data was demonstrated upon audio signal augmentation. In summary, the results affirm that the audio signal augmentation approach empowered the CNN models to predict bolt looseness in data-deficient scenarios accurately.

show abstract

Section: Cnns For Bolt Looseness Classificationmentioning

confidence: 99%

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Chelimilla,

Chinthapenta,

Korla

2024

Smart Mater. Struct.

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

A pipeline corrosion detecting method using percussion and residual neural network

Yang,

Ji,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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.

show abstract

“…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%

“…Moreover, the final obtained features are not always effective when faced with unknown working conditions or application scenarios [ 37 ]. The Convolutional Neural Network (CNN) is one of the representative algorithms of deep learning, which can automatically extract waveform features and can learn inherent features of signals to perform more accurate classification and identification of fault diagnosis [ 10 ]. In recent years, CNNs have shown great potential in structural damage identification.…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhang

Zhou³

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Cited by 10 publications

References 37 publications

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

A pipeline corrosion detecting method using percussion and residual neural network

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

Contact Info

Product

Resources

About