When a defect occurs in a physical asset, it’s often not immediately detectable by operators. In fact, in some cases the defects are not visible to the naked eye. However, from the moment a defect occurs until it is found, there is a risk that the defect will grow in severity, and possibly transition into a failure, resulting in reduced or halted production. At a municipal transit company, the Nondestructive Testing (NDT) team uses specialized equipment to inspect the train tracks and identify the location and severity of any defects. Due to the limited hours during which the team can perform their work, the whole subway system can only be tested once per year. Using their data on train tracks and found defects, we investigate efficient ways to use the NDT team’s fixed resources in order to improve the reliability of the train track system.
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