Join PEMAC for our next lunch and learn webcast taking place on Friday, November 29that 12:00 pm Eastern Time Zone. Participants will gain tools and tips can use to strive for excellence in asset management, maintenance and reliability.
Topic: Applications of Machine Learning in the Field of Reliability and Maintenance Optimization
It seems like machine learning and artificial intelligence are all the rage in the news and other media. The applications appear to be broad, including economics, medicine and even the environment. As the cost of data collection and storage decreases, there is an abundance of information that could be hiding in our maintenance data. The field of maintenance and asset management has come a long way in making the most of our CMMS and sensor data, but more information can be unlocked through machine learning. What is machine learning? How can maintenance and asset management benefit from machine learning?
In this presentation, we'll discuss several machine learning algorithms in a way that's accessible and relevant to maintenance and reliability practitioners: a classification method, a clustering method, and a neural network method. Each method will be partnered with direct applications to real-life maintenance problems, including lessons learned and potential uses in other contexts.
Speaker: Janet Lam, Assistant Director, Centre for Maintenance Optimization and Reliability Engineering (C-MORE)
Dr. Janet Lam holds a PhD in industrial engineering from the University of Toronto. She has been working in the field of maintenance optimization since 2008, with an emphasis on optimal scheduling of inspections for condition-based maintenance. More recently, her research interests have extended to machine learning approaches for maintenance and asset management. Through her work at C-MORE, she has had the opportunity to apply academic research directly with industry partners, including those in mining, utilities, transportation, and the military. As the assistant director of C-MORE, Janet is involved with cultivating strong relationships with industry partners and developing maintenance engineering resources that are both useful and current.
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