4.7 Article

Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network

Related references

Note: Only part of the references are listed.
Article Engineering, Environmental

A CNN-based transfer learning method for leakage detection of pipeline under multiple working conditions with AE signals

Pengqian Liu et al.

Summary: This study proposes a convolutional neural network-based transfer learning method for pipeline leakage detection under multiple working conditions. The results show that the proposed feature-based CNN-TL method outperforms parameter-based CNN-TL and traditional CNN methods, achieving accurate detection of pipeline leaks under various working conditions.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2023)

Article Automation & Control Systems

A Novel Approach for Surface Integrity Monitoring in High-Energy Nanosecond-Pulse Laser Shock Peening: Acoustic Emission and Hybrid-Attention CNN

Zhifen Zhang et al.

Summary: This article proposes a novel method for real-time evaluation of surface quality in laser shock peening (LSP) based on multiple acoustic emission (AE) technique and hybrid attention-based convolutional neural networks (HACNN). A new quality index called surface hardness integrity (SHI) is constructed, and a linear relationship between SHI and AE is demonstrated. A feature extraction method called wavelet packet energy cepstrum (WPEC) is proposed and combined with HACNN. The method achieves a high average accuracy of 99.33% for identifying four types of SHI and demonstrates sensitivity in high-frequency event onset and offset through visualization of gradient weights.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2023)

Article Engineering, Industrial

Leakage diagnosis and localization of the gas extraction pipeline based on SA-PSO BP neural network

Jie Zhou et al.

Summary: This study proposed a pipeline leakage diagnosis method for the main gas extraction pipeline in coal mines based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) collaborative optimization Back Propagation Neural Network (BPNN). The SA-PSO BPNN leakage diagnosis model was established and its reliability was verified by mapping the location of the leakage point and monitoring value. The results showed that the SA-PSO BPNN model had higher accuracy in identifying leakage compared to other models. The diagnostic accuracy of the SA-PSO BPNN model was 93.33% through the verification samples.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2023)

Article Engineering, Multidisciplinary

Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model

Jiaxing Xin et al.

Summary: Accurate and quantitative characterization of deformation in oil and gas pipelines is essential for pipeline integrity management. This paper proposed a novel ACM-based technique to detect the deformation, revealing relationships between deformation factors and waveform signals. By using features and a SSA-BP algorithm, the proposed method efficiently characterizes pipeline deformation within a mean relative error of 10%.

MEASUREMENT (2022)

Article Chemistry, Analytical

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Sajjad Ahmad et al.

Summary: This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The technique extracts global and local features from acoustic images obtained through continuous wavelet transform, and applies a shallow artificial neural network to identify the pipeline leak state.

SENSORS (2022)

Article Computer Science, Information Systems

Multi-source information fusion to identify water supply pipe leakage based on SVM and VMD

Zhoufeng Wang et al.

Summary: This study proposed a method for leakage detection based on VMD and SVM with multi-source information fusion. By selecting and fusing eigenvectors, the method effectively recognized water pipe leaks and other operating conditions with a significantly improved accuracy rate compared to traditional methods.

INFORMATION PROCESSING & MANAGEMENT (2022)

Article Engineering, Mechanical

Prediction model of natural gas pipeline crack evolution based on optimized DCNN-LSTM

Bin Wang et al.

Summary: This paper proposes a time series prediction method based on acoustic emission signals, which can effectively predict crack evolution in natural gas pipelines. It improves the prediction accuracy and is significant for the safe operation of gas projects.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Engineering, Multidisciplinary

A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

Qi Li et al.

Summary: A novel SSA-CNN method is proposed for oil pipeline leakage detection, which converts input data to two-dimensional matrix and optimizes CNN parameters using SSA algorithm, enhancing the neural network's extraction of eigenvalues. Experimental results show an accuracy rate of 98.67%.

MEASUREMENT (2022)

Article Engineering, Mechanical

Crack detection and localization in a fluid pipeline based on acoustic emission signals

Thang Bui Quy et al.

Summary: This paper presents a novel approach for crack detection and localization in high-pressure fluid pipelines using acoustic emission signals, which involves scanning peaks, filtering noise, localizing emission sources through time difference of arrival technique, and eliminating false emission sources by considering wave energy attenuation characteristics. By observing the distribution of emission sources according to position and time, the method can indicate the location of irregular structural changes based on emission source distribution and density along the pipeline.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)

Article Computer Science, Artificial Intelligence

Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

Mohsen Motahari-Nezhad et al.

Summary: This paper discusses the estimation of the remaining useful life of angular contact ball bearings using time-domain signal processing methods, introducing 60 time-domain features for fault detection and utilizing the IDE method for feature dimensionality reduction. The KNN algorithm is used for bearing classification based on selected features, with results showing high precision in fault detection. The study validates the performance of the KNN classifier with performance indices, highlighting the importance of features such as kurtosis in achieving high accuracy, precision, and specificity in bearing classification.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Article Energy & Fuels

Investigation on recognition method of acoustic emission signal of the compressor valve based on the deep learning method

Yangyang Zhang et al.

Summary: The paper utilizes Acoustic Emission (AE) technology combined with deep learning methods to successfully predict the dynamic characteristics of reciprocating compressor valves, providing an experimental and theoretical basis for fault diagnosis.

ENERGY REPORTS (2021)

Article Engineering, Multidisciplinary

Convolutional neural networks-based valve internal leakage recognition model

Shen-Bin Zhu et al.

Summary: The traditional methods for diagnosing valve internal leakage have limitations, leading to the proposal of a new method using convolutional neural networks to recognize valve internal leakage. Experimental results show that this method can effectively identify internal leakage signals, with a maximum prediction error of less than 3%, serving as a new approach for valve leakage diagnosis.

MEASUREMENT (2021)

Article Engineering, Environmental

Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector

Tianshu Xu et al.

Summary: This study proposes a pipeline leak identification method based on VMD and SVM, which effectively reduces the impact of collision noise on leak sound recognition and improves leak detection performance through improved mode decomposition and feature extraction techniques.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2021)

Article Geochemistry & Geophysics

Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification

Minghao Zhu et al.

Summary: This article proposes an end-to-end residual spectral-spatial attention network (RSSAN) for hyperspectral image classification, utilizing spectral and spatial attention mechanisms to extract effective features for classification and adaptive feature refinement, ultimately achieving superior classification accuracy on three HSI datasets.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

Article Engineering, Civil

Continuous Leak Detection and Location through the Optimal Mother Wavelet Transform to AE Signal

Shaofeng Wang et al.

JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE (2020)

Review Metallurgy & Metallurgical Engineering

Leak Detection System for Long-Distance Onshore and Offshore Gas Pipeline Using Acoustic Emission Technology. A Review

Anselemi B. Lukonge et al.

TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS (2020)

Article Engineering, Environmental

Sound-turbulence interaction model for low mach number flows and its application in natural gas pipeline leak location

Cuiwei Liu et al.

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION (2020)

Article Engineering, Civil

Deep Spatial-Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting

Shengnan Guo et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)

Proceedings Paper Mining & Mineral Processing

Automated Full Waveform Detection and Location Algorithm of Acoustic Emissions from Hydraulic Fracturing Experiment

Jose Angel Lopez Comino et al.

ISRM EUROPEAN ROCK MECHANICS SYMPOSIUM EUROCK 2017 (2017)

Article Automation & Control Systems

An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

Yaguo Lei et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)