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

多伦多皮尔逊国际机场是加拿大最大的机场，每年处理超过2500万名乘客，预计将大幅增长。机场的自动客运系统对于客运大楼（一号客运大楼和三号客运大楼）之间的乘客运输以及酒店和停车场等场外设施至关重要。这是一项关键的基础设施，对在机场运送乘客至关重要。该系统是一个电缆驱动系统，由复杂的组件组合组成，如轴承、齿轮和其他机电组件。为了确保该系统的顺利运行，需要进行连续或定期的维护，以便及时发现其部件的劣化或损坏等情况的任何变化。该项目旨在开发一种基于状态的维护系统，该系统可用于检测任何这些关键部件的早期故障（诊断），并估计其剩余使用寿命（预测），以便更有效地进行维护决策。对于诊断，将开发新的时频变换来可靠地表征瞬态事件和非平稳数据，并使用振动数据的离散表示来检测异常。统计模型如隐马尔可夫模型和基于贝叶斯理论的模型将用于预测。****此计划是申请人及其研究小组为降低整体维修成本及减少机场重要设施意外停机而制订维修策略的一部分。这将使大多伦多机场管理局能够更好地应对其乘客增长目标（预计到2030年将增长超过6000万），并与该地区的其他美国主要机场（如芝加哥）竞争。该项目还将为HQP在仪器仪表、信号处理和可靠性理论领域的培训提供宝贵的机会。****

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.****This project is a part of a wider effort by the applicant and his 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.****