小型水平轴风力发电机故障诊断的实验与信号处理技术
Experimental and Signal Processing Techniques for Fault Diagnosis on a Small Horizontal-Axis Wind Turbine Generator
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中文总结 AI 辅助
针对被忽视的小型HAWT发电机状态监测问题，通过风洞与试验台测试采集振动数据，用时频域分析结合谱相干分析诊断轴承保持架故障。
中文摘要 AI 辅助
小型水平轴风力发电机（HAWT）是一项具有非平凡临界点的技术，主要因为它面向家庭使用，因此必须将廉价制造与控制和复杂流动条件（尤其是城市环境）下的良好效率相结合。因此，动态控制优化以及噪声和振动抑制是这类技术的紧迫问题。尽管小型HAWT的发电机占总质量的比例不可忽略，机电耦合具有重要意义，但小型HAWT发电机的状态监测是一个被忽视的主题。本研究是对一台最大输出功率3kW、转子直径2米的HAWT永磁发电机的损伤诊断案例研究。实验分析通过风洞测试和发电机试验台进行，其中受损和未受损的发电机在不同转速下被驱动。风洞中通过轴后轴承附近的径向加速度计采集振动测量值，试验台中通过固定在径向位置（以便与前后轴承对齐）的单轴加速度计采集振动测量值。试验台数据对于研究振动频谱的低频尾特别有用，而轴承的特征频率位于该频段。对实验数据在时域和频域进行分析以提取特征，尤其使用谱相干分析来诊断支撑发电机的轴承保持架故障。
英文摘要
Small HAWT is a technology characterized by non-trivial critical points, basically because it is targeted for domestic use and therefore cheap manufacturing and control must conjugate with good efficiency under possibly complex flow conditions (especially in urban environment). Therefore, dynamical control optimization and noise and vibration mitigation are pressing issues for this kind of technology. Despite it is peculiar of small HAWTs that the generator constitutes a non-negligible fraction of the total mass and therefore the electromechanical coupling is relevant, condition monitoring of small HAWT generators is an overlooked topic. The present work is a test case study of damage diagnosis on a permanent magnet generator of a HAWT having 3 kW of maximum power and 2 meters of rotor diameter. The experimental analysis is conducted through wind tunnel tests and on a generator test rig where a damaged and an undamaged generators have been driven at different rotational speeds. Vibration measurements are collected in the wind tunnel through radial accelerometers near the rear bearing of the shaft and in the test rig through uni-axial accelerometers (fixed in radial positions, in order to be aligned with front and rear bearings). The test rig data result being particularly useful for studying the low-frequency tail of the vibration spectrum, where the characteristic frequencies of the bearing are located. The experimental data are analyzed in the time and frequency domain for feature extraction: a fault in the cage of the bearing supporting the generator is diagnosed using in particular the spectral coherence analysis.
