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将 PHM 扩展到整个飞机上:访问其他数据集如何增强预测性维护.pdf

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1、 2024 Collins Aerospace.All rights reserved.Expanding PHM Across the Aircraft:How Accessing Additional Data Sets Can Enhance Predictive MaintenanceRTX Corporation(Corporate)-Company Use 2024 Collins Aerospace.|Unrestricted Content.|This document does not include any export controlled technical data.

2、Expanding PHM Across the Aircraft2Ian is the General Manager for the Data,Applications,and Platform Solutions portfolio for Collins Aerospaces Connected Aviation Solutions group.The portfolio includes Collins Ascentia predictive health maintenance platform as well as solutions leveraging data analyt

3、ics to optimize enroute flights for fuel savings,connected aircraft eOperations enablement,and FlightAware.He has been with Collins for 8 years in a number of product and program leadership roles,all focused on connected aviation.He is based in Charlotte,North Carolina,USA.Seth leads the Tech Ops di

4、gital portfolio as part of the Connected Aviation Solutions(CAS)business unit at Collins Aerospace.In his role,he is responsible for Ascentia,Collins predictive maintenance solution that focuses on applying AI/ML to aircraft sensor data to derive predictive alerts allowing operators to avoid unsched

5、uled interruptions.While at Collins,Seth has expanded Ascentia footprint which now encompasses 70+customers and over 3,500 tails.Prior to Seths time at Collins,he spent 15 years at American Airlines serving in various roles throughout engineering and Tech Ops.His most recent role was the Director of

6、 Reliability,Aircraft Software and Predictive Maintenance Engineering.He is based in Cleveland,Ohio,USA.2024 Collins Aerospace.|Unrestricted Content.|This document does not include any export controlled technical data.Evolution in the Industry up till today50 years of the industry dealing with Aircr

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本文主要探讨了飞机健康管理和预测性维护(PHM)的扩展应用,以及通过访问更多数据集如何提高预测性维护的准确性。作者Ian和Seth,分别是Collins Aerospace公司数据、应用和平台解决方案部门的总经理和Tech Ops数字组合的负责人,他们有着丰富的航空行业经验。文章首先回顾了过去50年航空行业处理飞机数据的发展历程,从1980年代的中央维护计算机系统(CMCS)到21世纪的AI/ML高级云计算,飞机的健康管理已经发展成为航空公司稳定解决方案。然而,文章指出,尽管现在全球商业飞机舰队中有近60%通过AID或无线QAR连接,但大多数飞机在飞行中只记录了可用数据点的0.1%以下,存在大量未记录的“暗系统”。文章强调了开发低代码定制警报、分析开发者工作室、分析组合在5个机队上的重要性,以及自动新签名评估工具在识别飞行数据中的模式并快速转化为预测分析方面的作用。最后,文章提出了未来的发展方向,包括充分利用飞机上的无线边缘计算和智能产品数据,以支持PHM/IAHM。
如何通过扩展飞机健康管理系统来提高预测性维护的效率? 航空业在预测性维护方面取得了哪些重大进展? 如何利用AI/ML技术优化飞机系统的数据分析和维护操作?
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