[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120259-en":3,"doc-seo-120259-105":30,"detail-sidebar-cat-0-en-105":94},{"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},120259,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Edge machine learning-based industrial fault detection - Thesis reviewer’s report","This thesis reviewer’s report evaluates a master thesis titled “Edge machine learning-based industrial fault detection” by Erik Pásztor. The reviewer judges the assignment as challenging yet fully fulfilled, confirming that requirements and primary goals were achieved. Methodology and technical approach are rated A-excellent, with clear discussion of edge-computing fault-detection methods, careful analysis of incremental-sensor signals, proficient edge-system software use, and effective mechanical gearbox modeling. The presentation quality, English, graphics, and citation practices are also assessed at a high standard, with an overall grade of A-excellent.","THESIS REVIEWER’S REPORT  \nI. IDENTIFICATION DATA  \nThesis title: Edge machine learning-based industrial fault detection  \nAuthor’s name: Erik Pásztor  \nType of thesis : master  \nFaculty/Institute: Faculty of Electrical Engineering (FEE)  \nDepartment: Department of Measurement  \nThesis reviewer: Ing. Milan Komárek, Ph. D.  \nReviewer’s department: STMicroelectronics  \nII. EVALUATION OF INDIVIDUAL CRITERIA  \n\n| Assignment challenging\u003Cbr>How demanding was the assigned project? |\n| --- |\n| Although we are currently witnessing an unprecedented surge in the utilization of applied machine learning techniques for fault detection problems, it is important to acknowledge that this scientific field is still relatively new. Keeping this in mind, the assigned task presents the author with challenging questions and tasks to tackle, as there is a limited pool of reported practical applications to draw upon for reference. |\n\n\n| Fulfilment of assignment fulfilled\u003Cbr>How well does the thesis fulfil the assigned task? Have the primary goals been achieved? Which assigned tasks have been incompletely covered, and which parts of the thesis are overextended? Justify your answer. |\n| --- |\n| All the requirements and objectives of the assignment have been successfully fulfilled. |\n\n\n| Methodology correct\u003Cbr>Comment on the correctness of the approach and/or the solution methods. |\n| --- |\n| The author has chosen the appropriate approach and methods in accordance with the assignment. |\n\n\n| Technical level A-excellent.\u003Cbr>Is the thesis technically sound? How well did the student employ expertise in the field of his/her field of study? Does the student explain clearly what he/she has done? |\n| --- |\n| The methods for fault detection utilizing edge computing were thoroughly discussed in the study. The specificities of the signal originating from the incremental sensor were also carefully examined and analyzed. The author effectively utilized the available software tools to achieve the defined goals on edge system. The implementation of the mechanical model to emulate the behavior of a real gearbox system was executed proficiently, resulting in the acquisition of data that closely resembles real-world scenarios. This data proved to be valuable for training the machine learning models and evaluating the efficiency of fault detection methods. |\n\n\n| Formal and language level, scope of thesis A-excellent.\u003Cbr>Are formalisms and notations used properly? Is the thesis organized in a logical way? Is the thesis sufficiently extensive? Is the thesis well-presented? Is the language clear and understandable? Is the English satisfactory? |\n| --- |\n| The level of language used in the reviewed text is of a high standard. Grammar errors are infrequent and do not significantly affect the overall quality. The graphical representation of the presented data is flawless and does not raise any concerns. |\n\n\n| Selection of sources, citation correctness A-excellent.\u003Cbr>Does the thesis make adequate reference to earlier work on the topic? Was the selection of sources adequate? Is the student’s original work clearly distinguished from earlier work in the field? Do the bibliographic citations meet the standards? |\n| --- |\n| The selection of sources made by the author was appropriate and relevant to the topic. The motivation for extending previous work in the field was clearly explained, and the citation of the sources was at an appropriate level. |\n\nTHESIS REVIEWER’S REPORT  \n\n| Additional commentary and evaluation (optional)\u003Cbr>Comment on the overall quality of the thesis, its novelty and its impact on the field, its strengths and weaknesses, the utility of the solution that is presented, the theoretical/formal level, the student’s skillfulness, etc. |\n| --- |\n| Please insert your comments here. |\n\nIII. OVERALL EVALUATION, QUESTIONS FOR THE PRESENTATION AND DEFENSE OF THE THESIS, SUGGESTED GRADE  \nSummarize your opinion on the thesis and explain your final grading. Pose questions th","cbCainQp0R7F3m8x","https://ap.wps.com/l/cbCainQp0R7F3m8x","pdf",107246,1,2,"English","en",105,"# Identification data\n## Evaluation of individual criteria\n## Overall evaluation and questions for defense","[{\"question\":\"What is the thesis title and who authored it?\",\"answer\":\"The thesis title is “Edge machine learning-based industrial fault detection,” authored by Erik Pásztor.\"},{\"question\":\"How were the assignment requirements and objectives assessed?\",\"answer\":\"All requirements and objectives were judged to have been successfully fulfilled.\"},{\"question\":\"What technical strengths did the reviewer highlight for the fault detection approach?\",\"answer\":\"The reviewer highlighted thorough discussion of edge-computing fault detection, careful examination of incremental-sensor signals, effective use of software tools, and successful mechanical gearbox modeling that produced valuable training and evaluation data.\"},{\"question\":\"What questions were raised during the presentation and defense?\",\"answer\":\"The reviewer asked about comparable published studies using IRC or similar signal sources, and whether a single sufficiently capable MCU (e.g., STM32F4x with Ethernet) could meet real-world industrial requirements instead of using three MCUs.\"}]","Edge machine learning-based industrial fault detection - 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