[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122078-en":3,"doc-seo-122078-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122078,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Driven Corrosion Detection and Classification in Pipelines","Pipeline corrosion poses major risks to operational safety and economic performance in the petrochemical industry. The research develops a robust detection and classification system by combining advanced signal processing with machine learning. Using positivist philosophy and a deductive approach, noisy vibration data from Fiber Bragg Grating (FBG) sensors along an oil pipeline are processed with CEEMDAN and Bhattacharyya Variance Distance to enhance signal quality. Features are clustered via K-means and classified for corrosion severity using KNN, SVM, and XGBoost, with XGBoost achieving perfect metric scores.","Machine Learning-Driven Corrosion Detection and Classification in Pipelines  \nby  \nLiwei Liu  \nSupervisor: Dr Gordon Dickers  \nProject submitted as part of the requirements for the  \naward of  \nMSc Software Engineering and Artificial Intelligence  \nAugust 2024  \nAbstract: Pipeline corrosion is a critical issue in the petrochemical industry, with significant implications for both operational safety and economic efficiency. This research focuses on developing a robust system for detecting and classifying pipeline corrosion using advanced signal processing techniques and machine learning algorithms. The study is grounded in a positivist research philosophy, employing a deductive approach to apply established theories in a real-world context.  \nData for this research was collected from a project conducted by a petrochemical company in China, where Fiber Bragg Grating (FBG) sensors were deployed along an oil pipeline to monitor vibrations caused by liquid flow. These vibrations were analyzed to detect potential corrosion. The raw sensor data, often noisy due to environmental factors, was processed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bhattacharyya Variance Distance (BVD) algorithms to enhance signal quality.  \nKey features indicative of pipeline condition were extracted and subjected to clustering analysis using the K-means algorithm, categorizing the data into distinct groups representing different levels of corrosion severity. Subsequently, classification models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost, were applied to predict corrosion severity. The XGBoost model demonstrated superior performance, achieving perfect accuracy, precision, recall, and F1 scores.  \nThe study also addresses the ethical considerations of data privacy and the responsible application of machine learning in industrial settings. The findings highlight the effectiveness of the proposed methodologies in accurately detecting and classifying pipeline corrosion, offering significant potential for improving pipeline maintenance and safety. Recommendations for further research include increasing the dataset size and exploring additional or hybrid models to further enhance system accuracy. Keywords: Pipeline Corrosion Detection; Machine Learning; Clustering Analysis; Kmeans; KNN; SVM; XGBoost  \nCONTENT  \n1 Introduction ................................................................................................................. 1  \n1.1 Background ...................................................................................................... 1  \n1.2 Aim and objectives...........................................................................................2  \n2. Literature Review.......................................................................................................4  \n2.1 Current Research Status of Corrosion Detection Sensor Technology .............4  \n2.1.1 Ultrasonic Testing Technology .............................................................4  \n2.1.2 Optical Fiber Sensing Technology ........................................................5  \n2.1.3 Magnetic Flux Leakage (MFL) In-Line Inspection Technology ..........5  \n2.2 Signal denoising algorithms.....................................................................5  \n2.3 Current Research Status of Corrosion Detection and Identification Algorithms................................................................................................................................ 9  \n2.3.1 Mode Decomposition .......................................................................... 10  \n2.3.2 Wavelet Transform .............................................................................. 11  \n2.3.3 Machine Learning ............................................................................... 12  \n2.4 Current Research Status of Corrosion Monitoring Sensors and Signal Processing Systems ........","cbCaig1Z7l2L35hY","https://ap.wps.com/l/cbCaig1Z7l2L35hY","pdf",2870353,1,91,"English","en",105,"# Introduction\n## Background\n## Aim and objectives\n# Literature Review\n## Current Research Status of Corrosion Detection Sensor Technology\n## Signal denoising algorithms\n## Current Research Status of Corrosion Detection and Identification Algorithms\n## Current Research Status of Corrosion Monitoring Sensors and Signal Processing Systems\n# Research Methodology\n## Introduction\n## Research Philosophy\n## Research Approach\n## Research Strategy\n## Time Horizon\n## Research Techniques and Procedures","[{\"question\":\"What problem does the research address in pipeline operations?\",\"answer\":\"It targets pipeline corrosion, which threatens operational safety and reduces economic efficiency in the petrochemical industry.\"},{\"question\":\"How is noisy sensor data processed before machine learning?\",\"answer\":\"Vibration data collected from Fiber Bragg Grating (FBG) sensors is denoised using CEEMDAN and Bhattacharyya Variance Distance to improve signal quality.\"},{\"question\":\"Which model performed best for classifying corrosion severity and how was it evaluated?\",\"answer\":\"XGBoost delivered superior results, reaching perfect accuracy, precision, recall, and F1 scores compared with KNN and SVM.\"}]","Machine Learning-Driven Corrosion Detection and Classification in Pipelines | 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