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: $ 4.52万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=691139
• 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，2018年处理了近5000万名乘客，预计将大幅增长。机场的旅客捷运系统、旅客隧道和自动扶梯等资产对机场的顺利运作至关重要，要确保这些资产顺利运作，就需要进行持续或定期的维修，以便及时发现状况的任何变化，例如部件的恶化或损坏。许多容易发生故障的关键部件有助于振动监测，但在许多情况下，其动态特性的异质性和缺乏故障数据使这项工作具有挑战性。该项目旨在开发一个整体的状态监测策略，它可以用来检测早期故障，这样的异构关键部件，维修决策可以更有效地进行。对于诊断，新型机器学习算法将与复杂的时频变换相融合，以可靠地检测瞬态事件，即使在非平稳环境中，并开发故障敏感的状态指标。这些状态指标将用于通知下游随机退化模型，从而在基于贝叶斯推理的单个数学框架内实现故障检测和预测。该项目是申请人研究团队在开发维护策略以降低关键机场设施的总体维护成本和计划外停机方面的更广泛努力的一部分。这将使大多伦多机场管理局能够更好地科普其乘客增长目标（预计到2030年将增长超过6000万），并与该地区的其他主要美国机场竞争（例如，芝加哥）。该项目还将为HQP在仪器仪表、信号处理和可靠性理论方面的培训提供宝贵的机会。