Data-driven condition-based maintenance models

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Manufacturing, service, and process-oriented companies can spend 25% to 50% of their total cost of operations on maintenance of critical equipment. Examples of these include mining companies, steel producers, airlines, pulp mills, oil producers, as well as many others. The unavailability of a critical piece of equipment when it is needed can result in severe financial loss to the company. Condition-based maintenance (CBM) is one of the key maintenance tactics employed within organizations with the goal of obtaining as much life out of the equipment as is economically justifiable before making the decision to replace or repair it.****Once CBM is identified as appropriate for a given maintenance situation, making optimal maintenance decisions depends on an understanding of the equipment's condition, the use of appropriate failure and decision models, the selection of a suitable optimization methodology, and a powerful implementation tool. The Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto has generated significant research outputs in all of these areas. C-MORE now seeks to extend and enhance these achievements in several directions.****We will advance the state of the art in the statistical interpretation of reliability data, mainly to deal with problems with missing information in the early parts of long-lived asset histories. We will investigate degradation models as they fit into frameworks for remaining life optimal decision calculations, along with further advances specifically for remaining life estimation. We will also expand our previous work on CBM with limited data using expert knowledge elicitation and Bayesian statistics.