[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118501-en":3,"doc-seo-118501-105":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},118501,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Fault Tracing Bot in Electric Drive Software Using Machine Learning - Master's Thesis","The Electric Drive Software Continuous Deployment (EDCD) team at Volvo Car Corporation pursues automation for analyzing build log data produced by continuous software integration for Electronic Control Units (ECUs). The work applies machine learning to create a fault tracing bot that extracts meaning from log files with minimum or zero human interaction. The pipeline starts with cleaning and preprocessing large, unstructured logs, then uses clustering to label data, and finally trains and compares multiple algorithms to predict classifications for newly ingested inputs.","Fault Tracing Bot in Electric Drive Software Using Machine Learning  \nMaster's Thesis in Electrical Engineering  \nHariharan Gopinath, Soundarya Jai Prakash  \nDEPARTMENT OF ELECTRICAL ENGINEERING  \nCHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2023  \n[www.chalmers.se](www.chalmers.se)  \nMaster’s thesis 2023  \nFault Tracing Bot in Electric Drive Software Using Machine Learning  \nHariharan Gopinath, Soundarya Jai Prakash  \nDepartment of Electrical Engineering Chalmers University of Technology Gothenburg, Sweden 2023  \nFault Tracing Bot in Electric Drive Software Using Machine Learning Hariharan Gopinath, Soundarya Jai Prakash  \n© Hariharan Gopinath, Soundarya Jai Prakash, 2023 .  \nSupervisor: Mathews Peter, Volvo Car Corporation.  \nAdvisor: Mohammad Farsi, Department of Electrical engineering ,Chalmers University of technology.  \nExaminer: Thomas Erikson, Department of Electrical engineering ,Chalmers University of technology  \nMaster’s Thesis 2023  \nDepartment of Electrical Engineering Chalmers University of Technology SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nTypeset in LATEX  \nPrinted by Chalmers Reproservice Gothenburg, Sweden 2023  \nAbstract  \nThe Electric Drive Software Continuous Deployment (EDCD) team at Volvo Car Corporation is working towards figuring out the automation of the analysis of the build log data obtained as a result of the continuous software integration for Electronic Control Unit (ECUs) using Machine Learning. Machine Learning is expected to be used in building a fault tracing bot, whose main goal is to analyse the provided log data and find the underlying meaning or information through it with minimum or zero human interaction. As this is an ongoing process and the creation of such log data will never really end, this project could make it easier for the analysis of big projects in getting instant data. At first, we are working on the data cleaning and the preprocessing of the big unstructured log data, followed by the labeling of the data by the clustering process. Lastly, the machine learning algorithm is used to predict the classification of the newly fed data. Many different machine learning algorithms have been implied to compare and get the best accuracy predicting algorithm among them.  \nAcknowledgements  \nWe would like to thank our supervisor, Mathews Peter (Volvo Cars) for his assistance in interpreting the logs and are also very thankful to our Advisor at Chalmers, Mohammad Farsi, for helping us by giving some great feedback during our meetings and for guiding us forward through this process. We would like to take this opportunity to thank Thomas Eriksson (Examiner) for the academic guidance he has provided. We are very grateful for all the academic support and opportunity that has been provided to us.  \nAlso, we would like to express our gratitude to our managers at Volvo Cars, Johan Zackrisson and Anna Dahl for being pliable with our requirements in this project and helping us throughout the process. This definitely would have not been the same without all the guidance and support we have been provided with.  \nHariharan Gopinath & Soundarya Jai Prakash, Gothenburg, May 2022  \nContents  \n1 Introduction 1  \n1. 1 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.3 Literature Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.4 Scope and Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.5 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.6 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n1.7 Report Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n1.8 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2 Theory 7  \n2.1 AI-based Anomaly Detection ....................... 7  \n2.2 Unsupervised Learning . . . . . . . . . . . . . . .","cbCaiqccLzlz2qHX","https://ap.wps.com/l/cbCaiqccLzlz2qHX","pdf",3209083,1,65,"English","en",105,"# Introduction\n## Purpose\n## Goal\n## Literature Study\n## Scope and Limitations\n## Problem Definition\n## Research Questions\n## Report Outline\n## Background\n# Theory\n## AI-based Anomaly Detection\n## Unsupervised Learning\n## Supervised Learning\n## Semi-supervised Learning\n## Natural Language Processing\n## Reducing Dimension\n## Evaluation\n# Methods\n## Research Environment\n## Data Collection\n## Data Understanding and Preparation\n## Data Pipeline\n## Word Embedding","[{\"question\":\"What problem does the machine learning fault tracing bot address?\",\"answer\":\"It automates analysis of build log data from continuous software integration for ECUs, aiming to uncover underlying information with minimal human effort.\"},{\"question\":\"How is the log data processed before model training?\",\"answer\":\"The approach performs data cleaning and preprocessing for large unstructured logs, then labels data using clustering before training classifiers.\"},{\"question\":\"How are machine learning algorithms evaluated in the project?\",\"answer\":\"Multiple machine learning algorithms are compared to obtain the best accuracy for predicting the classification of newly fed data.\"}]","Fault Tracing Bot in Electric Drive Software Using Machine Learning - 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