Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deformation characterization of oil and gas pipeline by ACM technique based on SSA-BP neural network model
Authors
Keywords
Deformation characterization, ACM technology, SSA-BP neural network, Oil and gas pipeline, In-line inspection
Journal
MEASUREMENT
Volume 189, Issue -, Pages 110654
Publisher
Elsevier BV
Online
2021-12-28
DOI
10.1016/j.measurement.2021.110654
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multi-component fault diagnosis of wheelset-bearing using shift-invariant impulsive dictionary matching pursuit and sparrow search algorithm
- (2021) Zhan Xing et al. MEASUREMENT
- Investigation of different failure modes in oil and natural gas pipeline steels
- (2020) Reza Pourazizi et al. ENGINEERING FAILURE ANALYSIS
- Crack characterization in ferromagnetic steels by pulsed eddy current technique based on GA-BP neural network model
- (2020) Zhenwei Wang et al. JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
- Application of generalized regression neural network optimized by fruit fly optimization algorithm for fracture toughness in a pearlitic steel
- (2020) Ling Qiao et al. ENGINEERING FRACTURE MECHANICS
- An effective method for differentiating inside and outside defects of oil and gas pipelines based on additional eddy current in low-frequency electromagnetic detection technique
- (2020) Lijian Yang et al. JAPANESE JOURNAL OF APPLIED PHYSICS
- Application of improved GRNN model to predict interlamellar spacing and mechanical properties of hypereutectoid steel
- (2020) Ling Qiao et al. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
- Probabilistic analysis of corroded pipeline under localized corrosion defects based on the intelligent inspection tool
- (2020) Djamel Zelmati et al. ENGINEERING FAILURE ANALYSIS
- Modeling and simulation on speed prediction of bypass pipeline inspection gauge in medium of water and crude oil
- (2020) Zengmeng Zhang et al. MEASUREMENT & CONTROL
- Experimentation for Sag and Dimension Measurement of Thin-Walled Tubes and Pipes Using Multi-Channel Ultrasonic Imaging System
- (2020) N. Pavan Kumar et al. JOURNAL OF NONDESTRUCTIVE EVALUATION
- Measurement of coating thickness using lift-off point of intersection features from pulsed eddy current signals
- (2020) Yao Wang et al. NDT & E INTERNATIONAL
- Influence of magnetic domain wall orientation on Barkhausen noise and magneto-mechanical behavior in electrical steel
- (2019) Fasheng Qiu et al. JOURNAL OF PHYSICS D-APPLIED PHYSICS
- Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
- (2019) Lianshuang Dai et al. Applied Sciences-Basel
- Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis
- (2019) Pavanaditya Badida et al. Journal of Natural Gas Science and Engineering
- A novel pulsed eddy current method for high-speed pipeline inline inspection
- (2019) Guanyu Piao et al. SENSORS AND ACTUATORS A-PHYSICAL
- Investigation of the Lift-off Effect on the Corrosion Detection Sensitivity of Three-axis MFL Technique
- (2018) T. Azizzadeh et al. Journal of Magnetics
- A new measurement system using magnetic flux leakage method in pipeline inspection
- (2018) Yavuz Ege et al. MEASUREMENT
- Experimental research on the precision of wheeled caliper arm for measuring pipeline deformation
- (2018) Xiaoxiao Zhu et al. MEASUREMENT
- A displacement sensing method based on alternating current magnetic flux measurement
- (2018) Jikai Zhang et al. MEASUREMENT SCIENCE and TECHNOLOGY
- Pipeline deformation monitoring using distributed fiber optical sensor
- (2018) Shihai Zhang et al. MEASUREMENT
- Fast reconstruction of defect profiles from magnetic flux leakage measurements using a RBFNN based error adjustment methodology
- (2017) Jian Feng et al. IET Science Measurement & Technology
- Modeling effects of alloying elements and heat treatment parameters on mechanical properties of hot die steel with back-propagation artificial neural network
- (2017) Yong Liu et al. JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL
- Magnetic Flux Leakage Signal Inversion Based on Improved Efficient Population Utilization Strategy for Particle Swarm Optimization
- (2017) Wenhua Han et al. RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING
- Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks
- (2016) Jeremy A. Buck et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Estimation of Depth and Length of Defects from Magnetic Flux Leakage Measurements: Verification with Simulations, Experiments, and Pigging data
- (2016) Mojtaba Rostami Kandroodi et al. IEEE TRANSACTIONS ON MAGNETICS
- Three-dimensional defect inversion from magnetic flux leakage signals using iterative neural network
- (2015) Junjie Chen et al. IET Science Measurement & Technology
- An experimental evaluation of the probe dynamics as a probe pig inspects internal convex defects in oil and gas pipelines
- (2015) Xiaolong Li et al. MEASUREMENT
- Experimental study on the probe dynamic behaviour of feeler pigs in detecting internal corrosion in oil and gas pipelines
- (2015) Xiaolong Li et al. Journal of Natural Gas Science and Engineering
- Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects
- (2009) N.B.S. Gloria et al. NDT & E INTERNATIONAL
- A Recursive Bayesian Estimation Method for Solving Electromagnetic Nondestructive Evaluation Inverse Problems
- (2008) Tariq Khan et al. IEEE TRANSACTIONS ON MAGNETICS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started