Predicting and Preventing Malfunctions: A Revolutionary Approach to Condition-Based Asset Performance Management (APM)

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Webinar details

In 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.

In 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).

• The webinar will be recorded and will be sent out to registered attendees afterwards.
• A certificate of attendance will be provided to attendees who request one near the end of the live webinar session.
• Please note: the time stated on this event is in UTC. You will need to convert this to your own time zone.

Key takeaways from this webinar

• Power system reliability
• Asset performance management (APM)
• Machine learning and POF prediction

Related courses

This webinar/topic relates to our school of Industrial Automation, Instrumentation and Process Control and is particularly found in the following courses:

• 52911WA Graduate Certificate in Internet of Things (IoT) for Engineering (Foundations)
• 52886WA Advanced Diploma of Industrial Automation Engineering
• 52872WA Advanced Diploma of Robotics and Mechatronics Engineering
• Online – Bachelor of Science (Industrial Automation Engineering)
• Online – Master of Engineering (Industrial Automation)

To learn more about tuition fees, please click here.

About the presenter

Dr. Naser Hashemnia, EIT Lecturer & Principal consultant at HitachiEnergy 

Naser 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.