Condition based monitoring of critical airport infrastructure: automated people mover at the Pearson International Airport in Toronto

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Pearson International Airport in Toronto is the largest airport in Canada, handling over 25 million passengers a year and projected to grow significantly. The automated people mover at the airport is vital to the conveyance of passengers between terminals (Terminal 1 and Terminal 3), and to off-site facilities such as hotel and parking lots. This is a critical infrastructure vital to the conveyance of passengers at the airport. This system is a cable-driven system, consisting of complex assemblages of components such as bearings, gears and other electro-mechanical components. Ensuring smooth operation of this system requires maintenance, either continuously or at regular intervals, so that any change in the condition like deterioration or damage of its components can be detected in a timely manner. This project is aimed at developing a condition based maintenance system, which can be used to detect incipient failures (diagnosis) in any of these critical components and to estimate their remaining useful life (prognosis) such that maintenance decisions can be undertaken more efficiently. For diagnostics, novel time-frequency transforms will be developed to reliably characterize transient events and non-stationary data, and to detect anomalies using discrete representations of vibration data. Statistical models such as hidden Markov models and models based on Bayesian theory will be used for prognosis.