_风力发电机组叶片故障诊断

说明：  风机叶片的裂纹和断裂是导致风机机组事故的重要因素之一，尽早诊断出风机叶片的 故障部位与故障程度，对安全生产具有意义重大。本文将叶片振动信号作为研究对象，利用小波分解方法对其进行信号分解，并与时域和频域方法处理结果进行对比分析，得出诊断结论。仿真结果表明: 小波分解方法可以更有效的获取故障特征信号，具有较高的故障诊断率。
(The crack and fracture of fan blade is one of the important factors that cause the accident of fan set. It is important for safety production to diagnose the fault location and failure degree of fan blade as soon as possible. In this paper, the vibration signal of the blade is taken as the object of study. The signal is decomposed by the wavelet decomposition method, and the results are compared with the time domain and frequency domain methods. The diagnosis conclusion is obtained. The simulation results show that the wavelet decomposition method can acquire the fault characteristic signal more effectively, and has higher fault diagnosis rate.)