BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260807T145415Z UID:20E86D45-B5B8-420C-9A46-12228A357042 DTSTART;TZID=Asia/Kolkata:20260814T190000 DTEND;TZID=Asia/Kolkata:20260814T200000 DESCRIPTION:Artificial Intelligence (AI) and Machine Learning (ML) are driv ing a new era of innovation across engineering and technology. One of thei r most impactful applications is in the predictive maintenance of engineer ing assets\, where data-driven insights can significantly improve reliabil ity\, availability\, and operational efficiency.\n\nTraditional maintenanc e strategies are typically either reactive\, where maintenance is performe d after a failure\, or preventive\, where maintenance is scheduled at fixe d intervals. While these approaches are widely used\, they can lead to une xpected downtime\, reduced equipment performance\, and unnecessary mainten ance costs due to over-servicing.\n\nPredictive Maintenance offers a smart er alternative. By leveraging AI and ML techniques\, the future health and remaining useful life of equipment can be estimated\, enabling maintenanc e to be performed only when needed and before critical failures occur.\n\n This technical talk will introduce the fundamentals of AI and Machine Lear ning\, including key learning paradigms and their practical applications. It will also explore emerging developments in the field\, including the ri se of Generative AI\, and discuss how these technologies are shaping the f uture of engineering analytics.\n\nThe concepts will be illustrated throug h a real-world case study involving the failure prediction and life estima tion of Insulated Gate Bipolar Transistors (IGBTs) used in wind turbine po wer converters. The presentation will cover the wind turbine system\, the mechanisms leading to IGBT degradation\, and the application of multiple A I/ML techniques for failure prediction and remaining life assessment. The session will demonstrate how modern AI-driven approaches can transform mai ntenance from a reactive activity into a proactive strategy\, improving as set reliability while reducing operational costs.\n\nSpeaker(s): Dr. Atanu Talukdar\, \n\nVirtual: https://events.vtools.ieee.org/m/571677 LOCATION:Virtual: https://events.vtools.ieee.org/m/571677 ORGANIZER:amiya87@gmail.com SEQUENCE:12 SUMMARY:From Reactive Maintenance to Predictive Intelligence: Applying AI a nd Machine Learning for Engineering Asset Failure Prediction URL;VALUE=URI:https://events.vtools.ieee.org/m/571677 X-ALT-DESC:Description: <br /><p class="MsoNormal"><span dir="LTR">Artifici al Intelligence (AI) and Machine Learning (ML) are driving a new era of in novation across engineering and technology. One of their most impactful ap plications is in the predictive maintenance of engineering assets\, where data-driven insights can significantly improve reliability\, availability\ , and operational efficiency.</span></p>\n<p class="MsoNormal"><span dir=" LTR">Traditional maintenance strategies are typically either reactive\, wh ere maintenance is performed after a failure\, or preventive\, where maint enance is scheduled at fixed intervals. While these approaches are widely used\, they can lead to unexpected downtime\, reduced equipment performanc e\, and unnecessary maintenance costs due to over-servicing.</span></p>\n< p class="MsoNormal"><span dir="LTR">Predictive Maintenance offers a smarte r alternative. By leveraging AI and ML techniques\, the future health and remaining useful life of equipment can be estimated\, enabling maintenance to be performed only when needed and before critical failures occur.</spa n></p>\n<p class="MsoNormal"><span dir="LTR">This technical talk will intr oduce the fundamentals of AI and Machine Learning\, including key learning paradigms and their practical applications. It will also explore emerging developments in the field\, including the rise of Generative AI\, and dis cuss how these technologies are shaping the future of engineering analytic s.</span></p>\n<p class="MsoNormal"><span dir="LTR">The concepts will be i llustrated through a real-world case study involving the failure predictio n and life estimation of Insulated Gate Bipolar Transistors (IGBTs) used i n wind turbine power converters. The presentation will cover the wind turb ine system\, the mechanisms leading to IGBT degradation\, and the applicat ion of multiple AI/ML techniques for failure prediction and remaining life assessment. The session will demonstrate how modern AI-driven approaches can transform maintenance from a reactive activity into a proactive strate gy\, improving asset reliability while reducing operational costs.</span>< /p> END:VEVENT END:VCALENDAR