BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Vilnius BEGIN:DAYLIGHT DTSTART:20250330T040000 TZOFFSETFROM:+0200 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T030000 TZOFFSETFROM:+0300 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250908T145256Z UID:B3E38861-6759-4C1E-AAEE-6CBCD2A406AB DTSTART;TZID=Europe/Vilnius:20250904T140000 DTEND;TZID=Europe/Vilnius:20250904T150000 DESCRIPTION:The impact of artificial intelligence (AI) is rapidly growing a nd is increasingly pivotal across a wide range of disciplines\, from innov ative scientific research to practical\, everyday applications. The powerf ul capabilities of AI—spanning data analysis\, predictive modeling\, and beyond—equip researchers and professionals with unparalleled tools to t ackle complex problems\, push the boundaries of scientific discovery\, and elevate productivity to unprecedented levels. This talk will explore the integration of AI in diagnosing motor faults and advancing motor design\, highlighting how AI can significantly enhance the reliability and performa nce of electric motors in green transportation. It will delve into the use of machine learning and deep learning models to predict and prevent motor failures (e.g.\, inter-turn short-circuits\, demagnetization\, and bearin g faults) [1]-[3]\, which is essential for ensuring safety and reliability in transportation and industry. Furthermore\, the talk will highlight AI- driven innovations in motor design [4]\, such as noise-reduction\, offerin g insights into how AI can revolutionize traditional motor systems and con tribute to ongoing improvements in predictive maintenance and design pract ices.\n\n[1] A. Mohammad-Alikhani\, B. Nahid-Mobarakeh\, and M. F. Hsieh\, “One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven F ault Detection in Electric Machines\,” IEEE Trans. Industrial Elect. vol . 71\, no. 3\, pp. 3083-3092\, Mar 2024.\n[2] A. Mohammad-Alikhani\, B. Na hid-Mobarakeh\, and M. F. Hsieh\, “Diagnosis of Mechanical and Electrica l Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolu tional Neural Network\,” IEEE Trans. Energy Conver.\, early access\, Nov 2024\, doi: 10.1109/TEC.2024.3490736.\n[3] K. J. Shih\, M. F. Hsieh\, B. J. Chen\, and S. F. Huang\, “Machine Learning for Inter-Turn Short-Circu it Fault Diagnosis in Permanent Magnet Synchronous Motors\,” IEEE Trans. Magn.\, vol. 58\, no. 8\, 8204307\, Apr 2022.\n[4] M. F. Hsieh\, L. H. Li n\, T. A. Huynh\, and D. Dorrell\, “Development of Machine Learning-Base d Design Platform for Permanent Magnet Synchronous Motor Toward Simulation Free\,” IEEE Trans. Magn.\, vol. 59\, no. 11\, 8204307\, Aug 2023.\n\nC o-sponsored by: VILNIUS TECH Faculty of Electronics\n\nSpeaker(s): \, Min- Fu Hsieh\n\nRoom: 153\, Bldg: P2\, Plytinės str. 25\, Vilnius\, Lithuania \, Lithuania LOCATION:Room: 153\, Bldg: P2\, Plytinės str. 25\, Vilnius\, Lithuania\, L ithuania ORGANIZER:medeisis@ieee.org SEQUENCE:14 SUMMARY:Lecture "Artificial Intelligence-Assisted Design and Fault Diagnosi s of Electric Motors for Green Transportation" URL;VALUE=URI:https://events.vtools.ieee.org/m/492461 X-ALT-DESC:Description: <br /><p>The impact of artificial intelligence (AI) is rapidly growing and is increasingly pivotal across a wide range of dis ciplines\, from innovative scientific research to practical\, everyday app lications. The powerful capabilities of AI&mdash\;spanning data analysis\, predictive modeling\, and beyond&mdash\;equip researchers and professiona ls with unparalleled tools to tackle complex problems\, push the boundarie s of scientific discovery\, and elevate productivity to unprecedented leve ls. This talk will explore the integration of AI in diagnosing motor fault s and advancing motor design\, highlighting how AI can significantly enhan ce the reliability and performance of electric motors in green transportat ion. It will delve into the use of machine learning and deep learning mode ls to predict and prevent motor failures (e.g.\, inter-turn short-circuits \, demagnetization\, and bearing faults) [1]-[3]\, which is essential for ensuring safety and reliability in transportation and industry. Furthermor e\, the talk will highlight AI-driven innovations in motor design [4]\, su ch as noise-reduction\, offering insights into how AI can revolutionize tr aditional motor systems and contribute to ongoing improvements in predicti ve maintenance and design practices.</p>\n<p class="text-sm">[1] &nbsp\;A. Mohammad-Alikhani\, B. Nahid-Mobarakeh\, and M. F. Hsieh\, &ldquo\;One-Di mensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detec tion in Electric Machines\,&rdquo\; IEEE Trans. Industrial Elect. vol. 71\ , no. 3\, pp. 3083-3092\, Mar 2024.<br>[2] &nbsp\;A. Mohammad-Alikhani\, B . Nahid-Mobarakeh\, and M. F. Hsieh\, &ldquo\;Diagnosis of Mechanical and Electrical Faults in Electric Machines Using a Lightweight Frequency-Scale d Convolutional Neural Network\,&rdquo\; IEEE Trans. Energy Conver.\, earl y access\, Nov 2024\, doi: 10.1109/TEC.2024.3490736.<br>[3] &nbsp\;K. J. S hih\, M. F. Hsieh\, B. J. Chen\, and S. F. Huang\, &ldquo\;Machine Learnin g for Inter-Turn Short-Circuit Fault Diagnosis in Permanent Magnet Synchro nous Motors\,&rdquo\; IEEE Trans. Magn.\, vol. 58\, no. 8\, 8204307\, Apr 2022.<br>[4] &nbsp\;M. F. Hsieh\, L. H. Lin\, T. A. Huynh\, and D. Dorrell \, &ldquo\;Development of Machine Learning-Based Design Platform for Perma nent Magnet Synchronous Motor Toward Simulation Free\,&rdquo\; IEEE Trans. Magn.\, vol. 59\, no. 11\, 8204307\, Aug 2023.</p> END:VEVENT END:VCALENDAR