[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121994-en":3,"doc-seo-121994-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},121994,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","A Percussion Method to Detect Erosion of Elbow Using Machine Learning Algorithms - Thesis Overview","Elbows are widely used in industries such as oil and gas to redirect flow in pipeline systems, but long service time causes erosion and wear that can thin walls and lead to piercing or bursting. This thesis develops a percussion-based sensing approach combined with machine learning to estimate elbow erosion rate using tapped percussion signals recorded by a smartphone. Features are extracted with PSD and MFCC, then modeled by KNN, DT, SVM, RNN, and clustering methods (k-means, GMM), achieving strong training and testing accuracy. Results demonstrate feasibility without dedicated sensors, supporting pipeline safety assurance.","A Percussion Method to Detect Erosion of Elbow Using Machine  \nLearning Algorithms  \nBy  \nLan Cao  \nA thesis submitted to the Department of Mechanical Engineering, Cullen College of Engineering  \nin partial fulfillment of the requirements for the degree of  \nMaster of Science  \nin Mechanical Engineering  \nChair of Committee: Gangbing Song  \nCommittee Member: Zheng Chen  \nCommittee Member: Xuemin Chen  \nCommittee Member: Matthew A. Franchek  \nUniversity of Houston  \nDecember 2022  \nCopyright 2022, Lan Cao  \nACKNOWLEGEMENTS  \nI would like to express my great appreciation to Dr. Gangbing Song for all of his supporting during my graduate study at the University of Houston (UH) . When I decided to apply for graduate school back in 2020, I planned to attend an online Master degree program for Mechanical Engineering at a different university. Dr. Song successfully persuaded me to apply to the one at the University of Houston and do the thesis option instead of the full courses option. During the two and half years of my academic and research study at UH, Dr. Song presented me his previous research on smart materials and ongoing research on solving mechanical engineering problems with machine learning methods, which I assumed had only belonged to the computer science applications, such as YouTube video watching, Google translation, and video gaming. Dr. Song's guidance really expanded my view on integration of machine learning into many fields, not limited to mechanical engineering, but also to civil, chemical and medical industry as well. I graduated from UH with my Bachelor's degree of Mechanical Engineering in 2014, and I was astonished that the technology had made such a great progress since then. Throughout my thesis work, Dr. Song gave me close guidance and many suggestions when I was facing numerous challenges to improve machine learning results.  \nAlso I would like to offer my special thanks to my defense committee members Dr. Zheng Chen, Dr. Xuemin Chen and Dr. Matthew A. Franchek for taking their time to read my thesis and serve on my defense committee.  \nLast but not least, thanks to Mr. Ji’An Chen, a Ph.D. student in Dr. Song’s Smart Material and Structure Lab. We did experiment together and he helped to train me on computer science programming.  \nABSTRACT  \nElbows are widely used in many industries, especially in oil and gas industry. The purpose of elbow is to change the flow direction in pipeline systems. In some severe applications, elbows are employed to transport abrasive high-pressure multiphase flow medium. With the increase of the service time, the wall thickness of the elbow will become thinner due to erosion and wear, which may lead to piercing or bursting of the high-pressure piping system and cause negative impacts on both the economy and the environment.  \nA novel method of using percussion and machine learning to detect the rate of elbow’s erosion was developed and discussed in this thesis. Three sets of elbow and pipe assembly were used as test specimens. Then, six different erosion levels were simulated by grinding off mass from the internal wall of the elbows. The elbow bottom location, where the simulated erosion was, was tapped to generate the percussion sound, which was recorded by a smart phone. The power spectral density (PSD) and mel-frequency cepstral coefficient (MFCC) were employed to extract features from the percussion sound.  \nThe k-nearest neighbor (KNN), the decision tree (DT), and the support vector machine (SVM) were implemented with PSD features to learn the training samples and predict test samples. By using the above three basic machine learning methods, the experiment achieved an average of 90% accuracy on training data and 80% on testing data. Then, the recurrent neural network (RNN), a deep learning method, was  \nimplemented with MFCC features to learn and train the data. This method achieved 100% accuracy on training data and 97% on testing data. Finally, the unsupervised clustering","cbCaildHUuzLtY9F","https://ap.wps.com/l/cbCaildHUuzLtY9F","pdf",2300379,1,66,"English","en",105,"# Introduction\n## Elbow erosion and detection context\n# Related Work\n## Percussion-based detection method\n## Sound feature selection and machine learning algorithms\n# Research Methodology and Methods\n## Experiment setup and data generation\n## Feature extraction and model training\n## Evaluation and results","[{\"question\":\"What problem does the percussion method target in pipeline systems?\",\"answer\":\"It addresses elbow wall thinning caused by erosion and wear, which can eventually lead to piercing or bursting in high-pressure piping systems.\"},{\"question\":\"How is the percussion signal collected and processed?\",\"answer\":\"The simulated erosion area is tapped to generate percussion sound, recorded by a smartphone, then analyzed using PSD and MFCC feature extraction.\"},{\"question\":\"Which machine learning and deep learning models are used, and what accuracy is reported?\",\"answer\":\"KNN, decision tree, and SVM are trained with PSD features (about 90% training and 80% testing accuracy). An RNN using MFCC features achieves 100% training and 97% testing accuracy; k-means ranges from 49%–68% while GMM reaches about 76%. \"}]","A Percussion Method to Detect Erosion of Elbow Using Machine Learning Algorithms - Thesis Overview | PDF",1785808194,166,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-percussion-method-to-detect-erosion-of-elbow-using-machine-learning-algorithms-thesis-overview","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-percussion-method-to-detect-erosion-of-elbow-using-machine-learning-algorithms-thesis-overview/121994/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the percussion method target in pipeline systems?","Question",{"text":75,"@type":76},"It addresses elbow wall thinning caused by erosion and wear, which can eventually lead to piercing or bursting in high-pressure piping systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the percussion signal collected and processed?",{"text":80,"@type":76},"The simulated erosion area is tapped to generate percussion sound, recorded by a smartphone, then analyzed using PSD and MFCC feature extraction.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning and deep learning models are used, and what accuracy is reported?",{"text":84,"@type":76},"KNN, decision tree, and SVM are trained with PSD features (about 90% training and 80% testing accuracy). An RNN using MFCC features achieves 100% training and 97% testing accuracy; k-means ranges from 49%–68% while GMM reaches about 76%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]