基于叶尖计时的叶片外物打击辨识方法
收稿日期: 2023-12-29
修回日期: 2024-01-23
录用日期: 2024-02-21
网络出版日期: 2024-03-11
基金资助
国家自然科学基金重点项目(92160203);联合基金项目(U1808214)
Identification method of foreign object impact on blade based on blade tip timing
Received date: 2023-12-29
Revised date: 2024-01-23
Accepted date: 2024-02-21
Online published: 2024-03-11
Supported by
Key Program of National Natural Science Foundation of China(92160203);Joint Fund Project of China(U1808214)
外物打击是影响叶片安全运行的重要事件，打击事件的在线辨识有利于巨大灾难事故的提前预警。针对叶片服役过程中振动数据量大，且需要快速、准确地在线辨识打击事件的迫切需求，提出了一种利用峭度与振幅矩联合判据（KAM-BTT）的多工况下叶片外物打击辨识及打击时刻定位方法。基于叶尖计时法，只需要单个传感器即可实现。构建了考虑叶片安装误差、叶片失谐及传感器安装误差的外物打击振动响应模型，并进行了打击模拟，提出了基于叶片模型参数的外物打击辨识参数阈值获取方法。搭建了外物打击实验装置，完成了多转速下的外物打击辨识验证。实验结果表明，提出的打击辨识方法能凭借单传感器在0.047 s内实现打击事件的识别，为在线预警和数据管理提供了研究基础。
王树慧 , 王维民 , 李天晴 , 王珈乐 , 付振宇 , 张娅 . 基于叶尖计时的叶片外物打击辨识方法[J]. 航空学报, 2024 , 45(20) : 430051 -430051 . DOI: 10.7527/S1000-6893.2024.30051
Foreign Object Impact （FOI） is an important event that affects the safe operation of blades， and the online identification of FOI events is conducive to the early warning of huge disasters. To solve the urgent need of a large amount of vibration data during the service of the blade and the need to quickly and accurately identify the FOI event online， a method for the identification of FOI events and the location of the impact moment of the blade under multiple working conditions is proposed， using the joint criterion of Kurtosis and Amplitude Moment （KAM-BTT）. This method is based on the blade tip timing method， and requires only a single sensor. In this paper， a foreign object impact vibration response model considering the blade installation error， blade detuning and sensor installation error is constructed， the FOI simulation is carried out， and a method for obtaining the threshold of foreign object impact identification parameters based on the blade model parameters is proposed. The experimental device for FOI was set up， and the verification of FOI identification at multiple speeds was completed. Experimental results show that the KAM-BTT method proposed in this paper can realize the identification of FOI events within 0.047 s with a single sensor， which provides a research basis for online early warning and data management.
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