[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116905-en":3,"doc-seo-116905-105":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},116905,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Error Cause Analysis of Laboratory Results with the Help of AI - Master Thesis in Computer Engineering","Electronics laboratories must test many different devices, generating large characterization datasets for each unit. Traditional root cause analysis relies on expert manual inspection, making it costly and slow. Rule-based automatic evaluations using hard-coded signal-condition logic still require expert effort to tailor formulas per product and become difficult to scale due to data complexity. This thesis explores early steps toward a machine learning root cause analysis workflow, establishing a foundation for transitioning from expert-driven logic to data-driven ML methods.","Master Thesis in Computer Engineering  \nError Cause Analysis of Laboratory Results with  \nthe Help of AI  \nFebruary 27, 2023  \nMaster Candidate Supervisor  \nAndrea Matteazzi Prof. Gian Antonio Susto  \nStudent ID 2010655 University of Padova  \nExternal Supervisors  \nSandra Mack  \nInfineon  \nDr. Anja Zernig KAI  \nAcademic Year  \nTo my family and friends  \nAbstract  \nIn the electronics laboratory, a large amount of different devices need to be tested, and the characterization of each of them generates a large amount of data. Classical root cause analysis is inherently inefficient because it requires manual inspection by experts, turning out to be costly and time consuming. Furthermore, automatic evaluations through sequences of conditions for the signals, ending up in hard-coded logical formulas, are still inappropriate. This is due to the further necessity for experts, in order to design such formulas specifically for each different product, and to the complexity of the data itself, which may lead to the infeasibility of such approach.  \nFor these reasons, in this thesis, first steps towards a machine learning (ML) approach are investigated, laying the foundation into the transition to a ML root cause analysis approach.  \nContents  \nList of Figures xi  \nList of Tables xiii  \nList of Acronyms xix  \n1 Introduction 1  \n1. 1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.1. 1 Industrial Problem . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.1.2 Understanding the Data .................... 3  \n1.2 Research Questions ........................... 5  \n2 Machine Learning Theory 7  \n2.1 Machine Learning ............................ 7  \n2.1.1 Supervised and Unsupervised Learning ........... 7  \n2.1.2 Learning Algorithm and Cost Function ........... 8  \n2.1.3 Parameters and Hyperparameters .............. 9  \n2.1.4 Data Split and Generalization Error ............. 10  \n2.1.5 Overfitting and Underfitting ................. 11  \n2.2 Anomaly detection ........................... 13  \n2.3 Performance Measures . . . . . . . . . . . . . . . . . . . . . . . . . 18  \n2.3. 1 Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . 18  \n2.3.2 F1 Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20  \n2.3.3 Matthews Correlation Coefficient . . . . . . . . . . . . . . . 21  \n2.4 PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22  \n2.5 DBSCAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  \n2.6 Isolation Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27  \n2.7 K Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . 30  \nCONTENTS  \n2.8 Artificial Neural Network . . . . . . . . . . . . . . . . . . . . . . . 31  \n2.8.1 Deep Feed-Forward Neural Network . . . . . . . . . . . . . 32  \n2.8.2 Activation Function . . . . . . . . . . . . . . . . . . . . . . . 35  \n2.8.3 Regularization . . . . . . . . . . . . . . . . . . . . . . . . . 36  \n2.9 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . 38  \n2.9. 1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 38  \n2.9.2 Convolutional and Locally Connected Layers . . . . . . . . 42  \n2. 10 Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42  \n2.10.1 Autoencoders in Anomaly Detection . . . . . . . . . . . . . 43  \n2.10.2 Dense Autoencoder . . . . . . . . . . . . . . . . . . . . . . . 46  \n2.10.3 Convolutional and Locally Connected Autoencoders . . . 46  \n2.10.4 Variational Autoencoder . . . . . . . . . . . . . . . . . . . . 47  \n3 Model Analysis and Experimental Results 51  \n3.1 Unsupervised Approach ........................ 53  \n3.2 Semi-Supervised Approach ...................... 53  \n4 Conclusions and Future Works 67  \nReferences 69  \nList of Figures  \n1.1 Error cause analysis of different devices from a specific product . 1  \n1.2 Error cause analysis of different products .............. 2  \n1.3 Case study device and testing procedure ....","cbCaipBGHsmu1spd","https://ap.wps.com/l/cbCaipBGHsmu1spd","pdf",4136755,1,86,"English","en",105,"# Introduction\n## Motivation\n## Research Questions\n# Machine Learning Theory\n## Supervised and Unsupervised Learning\n## Anomaly Detection and Performance Measures\n## PCA\n## DBSCAN\n## Isolation Forest\n## K Nearest Neighbors\n## Artificial Neural Networks\n## Convolutional Neural Networks\n## Autoencoders\n# Model Analysis and Experimental Results\n## Unsupervised Approach\n## Semi-Supervised Approach\n# Conclusions and Future Works","[{\"question\":\"Why are classical root cause analysis methods inefficient in electronics laboratories?\",\"answer\":\"They require manual inspection by experts, which is costly and time consuming. Additionally, rule-based evaluations need experts to design product-specific hard-coded formulas.\"},{\"question\":\"What is the main goal of using machine learning in this thesis?\",\"answer\":\"To investigate initial steps for a machine learning-based root cause analysis approach, enabling a transition from expert-defined logic to data-driven methods.\"},{\"question\":\"Which machine learning concepts and models are covered in the theory section?\",\"answer\":\"The thesis includes core ML concepts, anomaly detection, performance measures, PCA, DBSCAN, Isolation Forest, k-nearest neighbors, and neural network models such as ANN, CNN, and autoencoders.\"}]","Error Cause Analysis of Laboratory Results with the Help of AI - Master Thesis in Computer Engineering | PDF",1785672420,217,{"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},"error-cause-analysis-of-laboratory-results-with-the-help-of-ai-master-thesis-in-computer-engineering","",{"@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/error-cause-analysis-of-laboratory-results-with-the-help-of-ai-master-thesis-in-computer-engineering/116905/",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},"Why are classical root cause analysis methods inefficient in electronics laboratories?","Question",{"text":75,"@type":76},"They require manual inspection by experts, which is costly and time consuming. Additionally, rule-based evaluations need experts to design product-specific hard-coded formulas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main goal of using machine learning in this thesis?",{"text":80,"@type":76},"To investigate initial steps for a machine learning-based root cause analysis approach, enabling a transition from expert-defined logic to data-driven methods.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning concepts and models are covered in the theory section?",{"text":84,"@type":76},"The thesis includes core ML concepts, anomaly detection, performance measures, PCA, DBSCAN, Isolation Forest, k-nearest neighbors, and neural network models such as ANN, CNN, and autoencoders.","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"]