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Machine Learning Approaches to Take Your Asset Management Strategies to the Next Level

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Original date: 
Friday, May 8, 2020

In our increasingly digitized and networked environment, the expectations for excellence in asset management are ever growing. While an abundance of maintenance and sensor data have become available, companies must develop the proper application of the data in their maintenance strategies. In this presentation, we’ll discuss the potential of your operational and maintenance data in the context of asset management, and explore different machine learning (ML) algorithms and how they may be leveraged to unleash hidden patterns in your asset management strategies. We’ll introduce some foundational topics required for ML, such as the taxonomy and data preparation steps critical to all ML approaches, the probability and statistics supporting ML, and how the evaluation of the quality of our models. C-MORE has actively applied machine ML methods to interesting real-world problems, such as the categorization of power generation units according to reliability characteristics, and anomaly detection in linear assets to optimize required maintenance actions. We’ll share a few of our case studies so participants can experience how ML methods can be used in maintenance, reliability and operations.

BoK Content Source: 
MainTrain 2020
BoK Content Type: 
Presentation Slides
Presentation Paper
Asset Management Framework Subject: 
03 Lifecycle Delivery, 3.05 Maintenance Delivery, 3.06 Reliability Engineering
Maintenance Management Framework Subject: 
03 Asset Strategy Management, 3.0 Asset Strategy Management General, 3.2 Performance Measurement & Optimization, 04 Tools and Tactics, 4.6 Condition Monitoring, 4.8 Predictive Maint. Techniques, 05 Maintenance & Reliability Engineering, 5.1 Stats Analysis / Analytical Methods, 5.2 Reliability Modelling, 09 Information Management, 9.0 Information Management General, 10 Continuous Improvement, 10.1 Metrics / KPIs
Author Title: 
Assistant Director
Author Employer: 
Centre for Maintenance Optimization and Reliability Engineering
Author Bio: 

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 applied 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.

Author 2 Title: 
Author 2 Employer: 
University of Toronto
Author 2 Bio: 
Dr. Chi-Guhn Lee is a Professor of Industrial Engineering and the Director of the Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto. Dr. Lee received a Ph.D. in Industrial and Operations Engineering at the University of Michigan, Ann Arbor, USA in 2001 and has been active in the areas such as Markov decision processes, reinforcement learning and deep learning applied to maintenance optimization, supply chain management and production systems. He has worked closely with private firms including LG, Nestle, IBM, General Motors, Magna International, State Grid Corp of China to name a few. He has played various roles in the academic community as well. He served as a co-chair of Workshop on Quantitative Finance and Risk Management 2012, a cluster-chair of Financial Engineering for Canadian Operational Research Society (CORS) Annual Meeting 2012 and 2013, and president of the Association of Korean-Canadian Scientists and Engineering (AKCSE) from 2013 to 2015. He served as a member of the Scientific Committee for the INFORMS MSOM 2015 conference, a member of the Technical Committee of the 26th International Conference on Flexible Automation and Intelligent Manufacturing 2016, a member of Program Committee for the Field Institute Workshop on Financial Optimization and Risk Management 2013 and 2015, a member of Steering Committee for the Field Institute Workshop on Optimization and Artificial Intelligence in Finance 2018, and a member of Program Committee for Spring World Congress on Engineering and Technology 2012. Prof. Lee has served as an associate editor for two academic journals: Enterprise Information Systems (a journal by Taylor and Francis Group with an impact factor 1.683) and International Journal of Industrial Engineering: Theory, Applications and Practice (homed at Simon Fraser University with a SCIE Impact Factor 0.537). He also served as a guest editor of Annals of Operations Research (a journal by Springer with an impact factor 1.864) from 2012 to 2015.