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基于瞬态启车数据的透平机械叶片健康监测方法 被引量:5

Health Monitoring Method of Turbomachinery Blades Based on Transient Start-up Data
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摘要 叶片的振动及其导致的疲劳断裂问题一直是限制透平机械向大型高参数化方向发展的主要障碍,对叶片裂纹进行及时诊断预警是防止事故的重要手段。本文研究通过启停车数据进行叶片裂纹故障诊断的方法,提出基于非线性最小二乘拟合和AR方法相结合的基于叶片同步振动的叶片状态参数精确辨识方法,准确捕捉叶片的固有频率,通过固有频率的变化辨别叶片裂纹故障并进行早期预警。开展了仿真和实验研究,结果表明,共振中心在4 800r/min左右,升速速率30r/min/s时,本文所提出的方法可以达到万分之二的测量精度。搭建了叶片振动测量与故障预警试验台,实验研究结果表明,本文所提出的方法可以进行叶片的动频的精确识别。 The vibration of the blade and the fatigue fracture are the main obstacle to the development of large and high parameterization turbomachinery. It is an important method to prevent the accident by blade crack timely diagnosis and early warning. In this paper, the method of fault diagnosis of blade crack is studied by using the start-up or shutdown data, and a method based on the combination of nonlinear least squares fitting and AR method is proposed to accurately identify the natural frequency of the blade. And the natural frequency changes can be used to identify the blade crack failure and early warning. The simulation results showed that when the resonant center is about 4 800 r/min and the acceleration rate is 30 r/min,the method proposed in this paper achieve the measurement accuracy of 2×10exp^-6. The experimental results showed that the method can accurately identify the dynamic frequency of the blade.
作者 张娅 陈康 王维民 屈维 Ya Zhang;Kang Chen;Wei-min Wang;Wei Qu(State Key Laboratory of Compressor Technology;Beijing Key Laboratory of Health Monitoring Control and Self-recovery for High-end Machinery,Beijing University of Chemical Technology,China)
出处 《风机技术》 2018年第6期75-82,共8页 Chinese Journal of Turbomachinery
基金 国家自然科学基金资助项目(51775030) 压缩机技术国家重点实验室开放基金项目
关键词 叶尖定时(BTT) 同步振动 参数识别 自回归法 Blade Tip-timing(BTT) Synchronous Vibration Parameter Identification AR Method
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