Holistic Vibration Condition Monitoring for Heterogeneous Airport Assets

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Pearson International Airport in Toronto is the largest airport in Canada, having handled nearly 50 million passengers in 2018 and projected to grow significantly. Assets such as the automated people mover, passenger tunnels and escalators at the airport are vital to the smooth functioning of the airport and ensuring their smooth operation requires maintenance, either continuously or at regular intervals, so that any change in the condition such as deterioration or damage to its components can be detected in a timely manner. Many of the critical components prone to failure are conducive to vibration monitoring, but the heterogeneity in their dynamic characteristics and lack of failure data in many cases makes this exercise challenging. This project is aimed at developing a holistic condition monitoring strategy, which can be used to detect incipient failures in such heterogeneous critical components such that maintenance decisions can be undertaken more efficiently. For diagnostics, novel machine learning algorithms will be fused with sophisticated time-frequency transforms to reliably detect transient events, even in non-stationary environments, and to develop fault-sensitive condition indicators. Such condition indicators will then be used to inform downstream stochastic degradation models to enable both fault detection and prognosis within a single mathematical framework based on Bayesian inference.This project is a part of a broader effort by the applicant's research team in developing maintenance strategies to reduce the overall maintenance cost and unplanned outages to critical airport facilities. This will enable Greater Toronto Airports Authority to better cope with their passenger growth targets (projected to grow over 60 million by 2030) and compete with other major US airports in the region (e.g., Chicago). This project will also provide valuable opportunities for HQP training in the areas of instrumentation, signal processing and reliability theory.