风机叶片故障预测的振动方法研究

风机工作中连续受到空气动力、惯性力等交变载荷的冲击,使得风轮桨叶产生不规则摆动和扭曲变形,异常振动和急剧变形将造成风机灾难性损毁,因此实时检测风机叶片整体运行状况显得尤其重要。提出基于三轴加速度传感器网络模型数据融合的风机叶片故障预测方法,通过对三维矢量分解、转换及提取技术反映风机整体运行模态,设计了数字信号处理技术的振动监控系统,对振幅超限的低频或超低频信号进行时域和频域分析,识别出了风机在4~30 Hz低频区域振动引起的故障率最高,并成功实现了风机叶片机械故障发生率75%的提前预测。

Research on vibration method of fan blade fault forecasting

During running,the fan blade continually suffers from the alternating load impact of the aerodynamic,which makes the fan blade and drive mechanism irregularly vibration and bend.The abnormal vibration and deformation will cause catastrophic damage to the fan,so the real-time monitoring for working status of fan blade is especially important.Fan vibration detection and monitoring mode based on 3-axis acceleration sensor net mode data fusion is introduced in this paper,which can real-time monitor the vibration of the fan blade,so as to analysis the overrun the low-frequency or ultra-low frequency vibration in time domain and frequency domain.It is found that the fault happens most easily to blade in low vibration frequency field about 4-30 Hz and over 75％ of blade machinery fault can be found in advance.