[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118545-en":3,"doc-seo-118545-105":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},118545,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning-Based Motor Health Prediction for Enhanced Lifespan Management","Research aims to build a predictive model that classifies motor condition (normal vs faulty) using vibration signals recorded from the motor. The study investigates whether fault conditions are learnable from vibration data and evaluates machine learning and deep learning strategies for different fault types. It emphasizes extracting meaningful vibration patterns and training models on engineered time, frequency, and time-frequency features. Experiments use a vibrational dataset collected in ABB laboratory conditions across varying speed-load settings, with results tied to data quality and improved by filtering, normalization, and transformation.","Ahsan Zahoor Khan  \nMaster Thesis  \nMachine Learning-Based Motor Health Prediction for Enhanced Lifespan  \nManagement  \nSchool of Technology and Innovation.  \nMaster’sThesis  \nSustainable and Autonomous Systems.  \n\n| UNIVERSITY OF VAASA\u003Cbr>School of Technology and Innovation.\u003Cbr>Author: Ahsan Zahoor Khan\u003Cbr>Title of the thesis: Master Thesis\u003Cbr>Degree: Master of Computer Science\u003Cbr>Discipline: Sustainable and Autonomous Systems\u003Cbr>Supervisor: Professor Petri Välisuo\u003Cbr>Professor Mohammed Elmusrati\u003Cbr>Principal Engineer Hauke Carstensen ABB Oy\u003Cbr>Year: 2025 Pages: 71 |\n| --- |\n| ABSTRACT:\u003Cbr>The aim of the research is to develop a model for predicting the condition of the motor (normal or faulty) by using the vibrational data recorded from the motor. The main question of the research is to find out,“Are these fault conditions predictable based on the vibrational data or Is vibrational data enough to predict different fault conditions of the motor using machine learning and deep learning?”\u003Cbr>The objective of the research are to find the core differences of the machine learning and deep leaning strategies in managing the fault prediction of the motors and compares different performance parameters i.e. accuracy of finding the condition of the motor. The study progresses with highlighting the key differences between machine learning and deep learning techniques and the set of principles required to make the predictions accurately.\u003Cbr>The research initiates with focusing on vibrational analysis of the motors and their key importance in detecting the faults of the motors. Most of the motor’s faults such as imbalance, poor lubrication, bearing defect etc. are related to vibrations. Therefore, by extracting the meaningful pattern of the vibrational data and then training of the model on such pattern could leads in predicting the fault of the motor.\u003Cbr>In this study, the vibrational dataset have been collected in controlled environment of ABB laboratories for varying speed-load conditions i.e. Normal or Faulty . The proposed methodology encompass both deep and machine learning algorithms in predicting the faults. Machine learning algorithms such as Support Vector Machine and the K-Nearest Means have been applied to the feature set fabricated from time, frequency, and time-frequency domain. In case of deep learning, Siamese Architecture with Feed Forward Neural Network have been applied to map between the feature vector and the desired output automatically.\u003Cbr>The results of the employed algorithms have demonstrated that they can assist in the prediction of motor faults. The prediction of the motor condition directly correlated with the quality and the quantity of vibrational data of the motor. Furthermore, effective data processing, such as filtering, normalization, and transformation, could improve the accuracy of the developed models. |\n\nKEYWORDS: Machine Learning, Deep Learning, Support Vector Machine, K-Nearest Means, Siamese Architecture, Siamese Architecture, Feed Forward Neural Network  \nContents  \n1 INTRODUCTION 8  \n1.1 BACKGROUND: 8  \n1.2 PROBLEM STATEMENT: 9  \n1.3 PURPOSE OF THIS STUDY: 12  \n1.4 SCOPE OF THE PROJECT: 13  \n1.5 IMPORTANCE OF THE STUDY: 14  \n1.6 RESEARCH QUESTIONS AND OBJECTIVES: 14  \n2 LITERATURE REVIEW 16  \n2.1 MACHINE LEARNING: 17  \n2.1.1 Machine Learning for fault diagnosis of the motor: 18  \n2.2 DEEP LEARNING: 24  \n2.2.1 Deep learning Model Development Process: 26  \n2.2.2 DL Techniques Used for Fault Diagnosis of Motor: 27  \n3 METHODOLOGY 30  \n3.1 RESEARCH DESIGN: 30  \n3.1.1 Experimental Setup: 30  \n3.1.2 Motor Specification: 30  \n3.1.3 Motor Placement: 31  \n3.1.4 Sensor Mounting: 32  \n3.2 DATA COLLECTED UNDER FAULT CONDITION: 32  \n3.3 DATA PREPROCESSING 35  \n3.3.1 Data Cleaning: 35  \n3.3.2 Feature Engineering: 35  \n3.3.3 Z-Score Normalization: 36  \n3.4 MODEL SELECTION: 36  \n3.4.1 Machine Learning & Deep Learning Models: 37  \n3.5 REASONING FOR THE SELECTION OF MODELS: 39  \n3.5.1 Complexities of Data: 40  \n3","cbCaismhIWAt8MqI","https://ap.wps.com/l/cbCaismhIWAt8MqI","pdf",2826836,1,71,"English","en",105,"# Introduction\n## Background\n## Problem Statement\n## Purpose of This Study\n## Scope of the Project\n## Importance of the Study\n## Research Questions and Objectives\n# Literature Review\n## Machine Learning\n### Machine Learning for fault diagnosis of the motor\n## Deep Learning\n### Deep learning Model Development Process\n### DL Techniques Used for Fault Diagnosis of Motor\n# Methodology\n## Research Design\n### Experimental Setup\n### Motor Specification\n### Motor Placement\n### Sensor Mounting\n## Data Collected Under Fault Condition\n## Data Preprocessing\n### Data Cleaning\n### Feature Engineering\n### Z-Score Normalization\n## Model Selection\n### Machine Learning & Deep Learning Models\n## Reasoning for the Selection of Models\n### Complexities of Data\n### Computational Efficiency\n### Complex Structure\n## Performance Evaluation Parameter\n# Research Result and Analysis\n## Data Cleaning Process\n### Sorting of Vibrational Data\n### Analysis of Data\n## Feature Extraction\n### Feature Extraction for ML\n### Feature Extraction for DL\n## Evaluations and Results\n### ML Performance\n### DL Performance\n# Conclusion","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis develops a model to predict motor condition (normal or faulty) using vibration data and examines whether vibration signals are sufficient to recognize different fault conditions through machine learning and deep learning.\"},{\"question\":\"How is the vibration data used to build predictive models?\",\"answer\":\"The approach extracts meaningful patterns from vibration signals and trains models using features engineered from time, frequency, and time-frequency domains. Preprocessing such as filtering, normalization, and transformation is used to improve accuracy.\"},{\"question\":\"Which machine learning and deep learning methods are applied?\",\"answer\":\"Machine learning methods include Support Vector Machine and K-Nearest Means applied to engineered feature sets. For deep learning, a Siamese Architecture combined with a Feed Forward Neural Network maps feature vectors to the desired output.\"}]","Machine Learning-Based Motor Health Prediction for Enhanced Lifespan Management | PDF",1785684076,179,{"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},"machine-learning-based-motor-health-prediction-for-enhanced-lifespan-management","",{"@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/machine-learning-based-motor-health-prediction-for-enhanced-lifespan-management/118545/",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","2026-08-02",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?","Question",{"text":75,"@type":76},"The thesis develops a model to predict motor condition (normal or faulty) using vibration data and examines whether vibration signals are sufficient to recognize different fault conditions through machine learning and deep learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the vibration data used to build predictive models?",{"text":80,"@type":76},"The approach extracts meaningful patterns from vibration signals and trains models using features engineered from time, frequency, and time-frequency domains. Preprocessing such as filtering, normalization, and transformation is used to improve accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning and deep learning methods are applied?",{"text":84,"@type":76},"Machine learning methods include Support Vector Machine and K-Nearest Means applied to engineered feature sets. For deep learning, a Siamese Architecture combined with a Feed Forward Neural Network maps feature vectors to the desired output.","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"]