BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Engineering Institute of Technology - ECPv6.17.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Engineering Institute of Technology
X-ORIGINAL-URL:https://www.eit.edu.au
X-WR-CALDESC:Events for Engineering Institute of Technology
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20240815T013000
DTEND;TZID=UTC:20240815T023000
DTSTAMP:20240815T133049Z
CREATED:20240730T092641Z
LAST-MODIFIED:20240815T133049Z
UID:51514-1723685400-1723689000@www.eit.edu.au
SUMMARY:Predicting and Preventing Malfunctions: A Revolutionary Approach to Condition-Based Asset Performance Management (APM)
DESCRIPTION:The live presentation of this event has already taken place. \nPlease view the PDF slides here or watch the video recording below:\n \nWebinar details\nIn the renewable energy industry\, a single component malfunction can significantly impact the entire network’s performance. Asset failures in solar and wind farms can bring down a whole network of infrastructure\, affecting thousands of customers and decreasing network reliability\, costing hundreds of thousands of dollars. Therefore\, there is a strong imperative to predict and avoid malfunctions in such highly connected systems. Sophisticated reliability models using new Machine Leaning (ML) techniques are proving to be a game-changer for asset performance management. Data and artificial intelligence are being used to predict malfunctions at any future point in time and facilitate the shift to condition than time-based maintenance. Early detection and more precise information can indicate which components or equipment will need to be repaired or replaced and when. This is allowing asset managers to plan maintenance efficiently and avoid unplanned and expensive disruptions. \nIn this webinar\, we outline how machine learning (ML)\, combined with industry expertise\, can estimate the probability of failure for specific failure modes and components. The methodology illustrates a specific failure mode using a large wind farm case study\, where a significant number of component failures occurred within a short space of time. The problem is solved using a two-step solution\, firstly predicting the future probability distribution of a parameter given its current value\, and secondly\, determining the probability of the malfunction given the predicted parameter value. This allows a standardized and more accurate approach for prognosticating malfunctions of technical components\, thus determining their remaining useful life (RUL). \n\nThe webinar will be recorded and will be sent out to registered attendees afterwards.\nA certificate of attendance will be provided to attendees who request one near the end of the live webinar session.\nPlease note: the time stated on this event is in UTC. You will need to convert this to your own time zone.\n\nKey takeaways from this webinar\n\nPower system reliability\nAsset performance management (APM)\nMachine learning and POF prediction\n\nRelated courses\nThis webinar/topic relates to our school of Industrial Automation\, Instrumentation and Process Control and is particularly found in the following courses: \n\n52911WA Graduate Certificate in Internet of Things (IoT) for Engineering (Foundations)\n52886WA Advanced Diploma of Industrial Automation Engineering\n52872WA Advanced Diploma of Robotics and Mechatronics Engineering\nOnline – Bachelor of Science (Industrial Automation Engineering)\nOnline – Master of Engineering (Industrial Automation)\n\nTo learn more about tuition fees\, please click here. \nAbout the presenter\n \nDr. Naser Hashemnia\, EIT Lecturer & Principal consultant at HitachiEnergy  \nNaser is a Principal Power Electrical Engineer (PhD) with over 12 years of experience in developing and analyzing the reliability of electrical networks and conducting system studies for various industries. His technical skills extend to conducting solution technical reviews\, designing reliability models\, and developing predictive analytics using machine learning. Naser has collaborated with numerous consultancies and academic institutions\, performing power system studies and protection\, as well as research and development.
URL:https://www.eit.edu.au/event/predicting-and-preventing-malfunctions-a-revolutionary-approach-to-condition-based-asset-performance-management-apm/
LOCATION:Online
CATEGORIES:Technical Engineering Topics Webinar
ATTACH;FMTTYPE=image/jpeg:https://www.eit.edu.au/wp-content/uploads/2024/06/14.11.2024_BannerImage.jpg
END:VEVENT
END:VCALENDAR