[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122552-en":3,"doc-seo-122552-105":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},122552,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Data Driven Prediction of Motor Status - Thesis for Master of Engineering in Automation Technology","Industrial AC motors operate widely across manufacturing and transportation, driving growing demand for improved energy efficiency and reduced waste. Strict European regulations require continuous optimization, including lowering idle-time losses caused by motors running without load. This master’s thesis develops a data-driven method to identify idle time and other operational states using tri-axial accelerometer vibration signals, preprocessing, feature extraction, and supervised machine learning model training. The best-performing One-Class SVM achieves 0.98 accuracy and 0.97 F1, enabling automated idle detection, real-time monitoring integration, and support for predictive maintenance and sustainable smart manufacturing.","Data Driven Prediction of Motor Status for Optimizing the Control Using Machine Learning  \nYi Zhang  \nDegree Thesis  \nThesis for a Master of Engineering (UAS) -degree Automation Technology  \nVaasa 2025  \nDEGREE THESIS  \nAuthor: Yi Zhang  \nDegree Programme and place of study: Automation Technology, Vaasa  \nSpecialisation: Intelligent system  \nSupervisor(s): Ray Pörn, Hauke Carstensen (ABB Oy)  \nTitle: Data Driven Prediction of Motor Status for Optimizing the Control Using Machine Learning  \n\n| Date: 3.5.2025 Number of pages: 53 | Appendices: 3 |\n| --- | --- |\n| Abstract\u003Cbr>The AC electric motor has been invented for over 200 years ago, and during this long period of development, it has become one of the most fundamental components of modern industry. Nowadays, it is used almost everywhere. From small pump components to large cruise ship steering systems, industrial AC motors are especially indispensable in a wide range of applications. Consequently, the less energy motors consume, the less environmental impact, which directly contributes to sustainable industrial practices. Particularly there are strict regulations that promote energy efficiency in Europe, which demand continuous improvement in motor design and operation. In addition to improving motor efficiency, another optimized energy consumption strategy is reducing AC motor idle time. That leads to wasteful power usage.\u003Cbr>This thesis aims to find a method for identifying idle time and other operational states of industrial AC motors. The first step is that a tri-axial accelerometer sensor needs to be installed to collect analog-to-digital converted (ADC) vibration data from the motor, ensuring that the dynamic mechanical behavior of the system is captured in sufficient detail. The second step is data preprocessing and feature extraction that need to be performed for machine learning. The third step is that three different machine learning models are developed and trained using labeled data to learn the characteristics between motor states. The final step is that, based on the best prediction result, the selected machine learning model is used to determine the operational state of the motor. Model performance is evaluated through parameters such as accuracy, precision, recall, and F1 score, which provide a comprehensive assessment of classification quality.\u003Cbr>The result shows that the One Class SVM model with the best thresholds achieved the highest accuracy of 0.98 and F1 score of 0.97 in identifying motor states. This confirms the feasibility of vibration machine learning for reliable motor state identification in practical industrial scenarios. The method enables automatic detection of idle periods and it can be integrated into real-time monitoring to support energy-saving strategies and to reduce unnecessary power consumption. Beyond energy efficiency, such approaches also contribute to predictive maintenance and sustainability in smart manufacturing environments. |  |\n\nLanguage: English  \nKey Words: AC motor, idle state, machine learning, ADC  \nABBREVIATIONS  \nAC motor ADC  \nAEAUCDAQ DC DOLDWTEVT FFT  \nidle IECiForest  \nIE4 Standard LV Motors  \nMEPS MSE OCSVM PCAPR RBF RMS ROC RQSVMUMAPVAEs WPT  \nAlternating Current motor Analog to Digital Converter  \nAutoencoders Area Under Curve Data Acquisition Direct Current  \nDirect-On-Line  \nDiscrete Wavelet Transform Extreme value theory  \nFast Fourier Transform Motor is running without load  \nInternational Electrotechnical Commission Isolation Forest  \nThe highest efficiency class for electric motos according to IEC Low Voltage Motors  \nMinimum Efficiency Performance Standards Mean Squared Error  \nOne Class Support Vector Machine Principal Component Analysis  \nPrecision – Recall Radial Basis Function Root Mean Square  \nReceiver Operating Characteristic Research Question  \nSupport Vector Machine  \nUniform Manifold Approximation and Projection Variational Autoencoders  \nWavelet Packet Transform  \nTable of Contents  \n1 Introduc","cbCaivY0ZVviSJtf","https://ap.wps.com/l/cbCaivY0ZVviSJtf","pdf",4072458,1,80,"English","en",105,"# 1 Introduction\n## 1.1 The client\n## 1.2 Purpose of thesis\n## 1.3 Limitations of thesis\n## 1.4 Research questions\n# 2 Theoretical Background\n## 2.1 Mechanical Vibration of Rotating Machinery\n## 2.2 Signal Processing and Feature Extraction\n## 2.3 Machine learning for IDLE Detection","[{\"question\":\"What problem does the thesis address for industrial AC motors?\",\"answer\":\"It targets identifying motor idle time and other operational states to reduce wasted power consumption and support energy-saving strategies under energy-efficiency regulations.\"},{\"question\":\"How is vibration data collected and prepared for machine learning?\",\"answer\":\"A tri-axial accelerometer is used to collect ADC-converted vibration signals, followed by data preprocessing and feature extraction to represent motor dynamics for learning.\"},{\"question\":\"Which model performed best and what were the key results?\",\"answer\":\"The One-Class SVM with tuned thresholds achieved the highest performance, reaching 0.98 accuracy and an F1 score of 0.97 for motor state identification.\"}]","Data Driven Prediction of Motor Status - Thesis for Master of Engineering in Automation Technology | PDF",1785811245,202,{"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},"data-driven-prediction-of-motor-status-thesis-for-master-of-engineering-in-automation-technology","",{"@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/data-driven-prediction-of-motor-status-thesis-for-master-of-engineering-in-automation-technology/122552/",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 thesis address for industrial AC motors?","Question",{"text":75,"@type":76},"It targets identifying motor idle time and other operational states to reduce wasted power consumption and support energy-saving strategies under energy-efficiency regulations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is vibration data collected and prepared for machine learning?",{"text":80,"@type":76},"A tri-axial accelerometer is used to collect ADC-converted vibration signals, followed by data preprocessing and feature extraction to represent motor dynamics for learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what were the key results?",{"text":84,"@type":76},"The One-Class SVM with tuned thresholds achieved the highest performance, reaching 0.98 accuracy and an F1 score of 0.97 for motor state identification.","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},[93,97,100,104,109,114,119,122,127,130,134],{"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":21,"slug":99},"Literature","literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]