[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125094-en":3,"doc-seo-125094-105":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":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},125094,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A fault detection strategy for an ePump during EOL tests based on a knowledge-based vibroacoustic tooland supervised machine learning classifiers","Fault detection for electric pumps during end-of-line (EOL) testing is addressed using acceleration and pressure pulsation signals to train supervised machine learning models. The approach reduces the risk of non-conforming units reaching the assembly line, where defects can still pass quality control yet later cause premature failure or abnormal noise during field operation. A knowledge-based vibroacoustic tool and a Python library generate informative features from the measured signals, then an ensemble learning algorithm combines multiple classifiers. Experimental validation on eighty ePumps reports above 95% accuracy, with comparisons supported by principal component analysis and a sensor sensitivity study.","Meccanica  \n[https://doi.org/10.1007/s11012-024-01754-w](https://doi.org/10.1007/s11012-024-01754-w)  \nA fault detection strategy for an ePump during EOL tests based on a knowledge‑based vibroacoustic tooland supervised machine learning classifiers  \nPasquale Borriello · Fabrizio Tessicini · Giuseppe Ricucci · Emma Frosina · Adolfo Senatore  \nReceived: 27 July 2023 / Accepted: 3 January 2024  \n© The Author(s) 2024  \nAbstract This paper presents a methodology for identifying faulty components in an electric pump during the end-of-line test based on accelerationsand pressure pulsation data used to train an ensemble learning algorithm based on supervised machine learning classifiers. Despite various quality control measures in pump manufacturing, some out-of-tolerance components can pass through and end up on the assembly line, potentially leading to premature failure or abnormal noise during real-field operation. Because of the high impact, it is very important to put in place actions to mitigate the risk of delivering non-conform units, even if properly working in terms of pressure-flow rate performances. In this paper, an innovative knowledge-based vibroacoustic tool together with a machine learning built-in Python® library have been used to post-process acceleration and pressure pulsations data to generate features, which are then used to train, and test several supervised machine learning algorithms. The ensemble  \nP. Borriello (*) · A. Senatore  \nDepartment of Industrial Engineering, University of Naples Federico II, Via Claudio, 21, 80125 Naples, Italy [e-mail: pasquale.borriello@unina.it](e-mail: pasquale.borriello@unina.it)  \nF. Tessicini · G. Ricucci  \nFluid-O-Tech S.R.L., Via Leonardo da Vinci, 40, 20094 Corsico, Milan, Italy  \nE. Frosina  \nDepartment of Engineering, University of Sannio, Piazza Guerrazzi, 21, 82100 Benevento, Italy  \nlearning algorithm combines the best classifiers to identify healthy electric pump units with high accuracy, achieving above 95% accuracy in an experimental test campaign carried out on eighty electric pumps. Results are compared using principal component analysis for dimensionality reduction, and a sensor sensitivity study is conducted.  \nKeywords External gear pump · Fault detection · The end of line (EOL) tests · Condition monitoring · Knowledge-based vibroacoustic model · Ensemble machine learning  \nList of symbols  \nΔp Delta pressure  \nσ Standard deviation  \nγ Regularization parameter  \nAI Artificial intelligence  \nAMR Amplitude of modulation at rotation  \nfrequency  \nAOR Amplitude of orders of rotation  \nApEn Approximate entropy  \nBLDC Brushless direct current  \nC Penalty parameter  \ncm Confusion matrix  \nDOE Design of experiments  \nDL Deep learning  \nDM Dummy model  \nDT Decision tree  \nDUT Device under test  \nDWT Discrete wavelet transform  \n1 3  \nEGM External gear machine  \nEOL End of line test  \nePump Electric pump  \nFFT Fast Fourier transformation  \nHP High pressure  \nKNN K-nearest neighbors  \nLLE Locally linear embedding  \nLP Low pressure  \nML Machine learning  \nMLP Multilayer perceptrons  \nMODWPT Maximal overlap discrete wavelet packet transform  \nMtry Number of the random input variables  \nNB Naïve Bayes  \nNIDAQ National instruments data acquisition Ntree Number of trees  \nNVH Noise, vibration, and harshness  \nPCA Principal component analysis  \nPE Permutation entropy  \nPACF Partial autocorrelation function  \nPHM Prognosis and health management  \nQ Volumetric flow rate  \nrbf Radial basis function  \nRF Random forest  \nR&D Research and development  \nRMS Rooth mean square  \nRUL Remaining useful life  \nSDG Stochastic gradient descent  \nSVM Support vector machine  \nt-SNE T-distributed stochastic neighbor  \nembedding TDV Tooth defect vibration  \nX  Input variable  \nTime series average of input variable  \n1 Introduction  \nFaulty components within a pump increase the risk of premature failure or unsatisfactory performance. In the context of an electrically driven external gear machine","cbCaicnyrmHuCYCw","https://ap.wps.com/l/cbCaicnyrmHuCYCw","pdf",2398503,1,26,"English","en",105,"# Introduction\n## Fault impact and need for early anomaly detection\n## Electric pumps and limitations of manufacturing quality control\n# Methodology\n## Feature generation from acceleration and pressure pulsation data\n## Knowledge-based vibroacoustic tool integration\n## Supervised machine learning ensemble classifiers\n# Experimental setup and evaluation\n## Dataset and testing campaign on electric pumps\n## Accuracy results\n## Dimensionality reduction using PCA\n## Sensor sensitivity study","[{\"question\":\"Why is fault detection during EOL testing important for electric pumps?\",\"answer\":\"Even when manufacturing quality controls are applied, some out-of-tolerance components can pass and reach the assembly line. Such defects may later lead to premature failure or abnormal noise during real-field operation, making early identification critical.\"},{\"question\":\"What data and tool are used to build the fault detection strategy?\",\"answer\":\"The method uses acceleration and pressure pulsation data from EOL tests. A knowledge-based vibroacoustic tool post-processes these signals to generate features for supervised learning.\"},{\"question\":\"How does the strategy combine machine learning models and how is performance assessed?\",\"answer\":\"An ensemble learning algorithm combines the best supervised classifiers to identify healthy ePump units. Results are compared using principal component analysis for dimensionality reduction and a sensor sensitivity study, achieving above 95% accuracy on eighty pumps.\"}]","A fault detection strategy for an ePump during EOL tests based on a knowledge-based vibroacoustic tooland supervised machine learning classifiers | PDF",1785896601,66,{"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-fault-detection-strategy-for-an-epump-during-eol-tests-based-on-a-knowledge-based-vibroacoustic-tooland-supervised-machine-learning-classifiers","",{"@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-fault-detection-strategy-for-an-epump-during-eol-tests-based-on-a-knowledge-based-vibroacoustic-tooland-supervised-machine-learning-classifiers/125094/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fault detection during EOL testing important for electric pumps?","Question",{"text":75,"@type":76},"Even when manufacturing quality controls are applied, some out-of-tolerance components can pass and reach the assembly line. Such defects may later lead to premature failure or abnormal noise during real-field operation, making early identification critical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and tool are used to build the fault detection strategy?",{"text":80,"@type":76},"The method uses acceleration and pressure pulsation data from EOL tests. A knowledge-based vibroacoustic tool post-processes these signals to generate features for supervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the strategy combine machine learning models and how is performance assessed?",{"text":84,"@type":76},"An ensemble learning algorithm combines the best supervised classifiers to identify healthy ePump units. Results are compared using principal component analysis for dimensionality reduction and a sensor sensitivity study, achieving above 95% accuracy on eighty pumps.","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"]