Holistic Vibration Condition Monitoring for Heterogeneous Airport Assets

异构机场资产的整体振动状态监测

• 批准号: 541430-2019
• 负责人: Narasimhan, Sriram
• 金额: $ 3.1万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=698921
• 关键词: Holistic; Vibration; Condition; Monitoring; Heterogeneous

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.

多伦多的皮尔逊国际机场是加拿大最大的机场，在2018年处理了近5000万乘客，预计将大幅增长。机场自动化人士，乘客隧道和自动扶梯等资产对于机场的平稳运行至关重要，并确保其平稳的运行需要持续或定期进行维护，因此，任何变质或对其组件的损害的任何变化都可以及时检测到。许多容易发生失败的关键组成部分有利于振动监测，但是在许多情况下，其动态特征和缺乏故障数据的异质性使此运动具有挑战性。该项目旨在制定整体状况监测策略，该策略可用于检测这种异质关键组件中的初期失败，以便可以更有效地执行维护决策。对于诊断，新颖的机器学习算法将与复杂的时频转换融合在一起，以可靠地检测瞬态事件，即使在非平稳环境中，并开发了对故障敏感的状况指标。然后，将使用此条件指标告知下游随机降解模型，以在基于贝叶斯推论的单个数学框架内实现故障检测和预后。 该项目是申请人研究团队在制定维护策略方面更广泛努力的一部分，以减少关键机场设施的整体维护成本和计划外的停机。这将使更大的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），并与该地区其他主要的美国机场（例如芝加哥）竞争。该项目还将在仪器，信号处理和可靠性理论领域的HQP培训中提供宝贵的机会。