Natili Francesco, Castellani Francesco, Astolfi Davide, Becchetti Matteo
Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy.
Sensors (Basel). 2020 Dec 19;20(24):7314. doi: 10.3390/s20247314.
The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates.
旋转机械转速的测量通常基于机械附着方式进行,例如在编码器中。然而,开发基于非接触视觉的方法来测量旋转机械的转速在各类应用中可能会很有意义。特别是,非接触式转子转速测量方法在风力涡轮机技术中,在非侵入式状态监测方法的背景下有若干潜在应用。本研究正是致力于此问题:提出了一种用于风力涡轮机转子转速估计的地面视频转速计测量技术和图像分析算法。该方法基于通过协方差矩阵对视频的参考帧与每一帧进行比较:由此获得一个协方差时间序列,通过频谱图将其转换到频域来估计转速。尽管系统存在内在的非平稳性以及可能的信号干扰,此过程仍保证了转速估计的稳健性。基于两个具有不同特性的实验环境对该方法进行了测试和讨论:前者是佩鲁贾大学风洞设施中的一个小型风力涡轮机模型(转子直径为0.45米),其关键在于高转速(高达1500转/分钟量级)。后者测试案例是一个工业风电场中转子直径为44米的风力涡轮机:在这种情况下,关键点在于测量是在非受控条件下获取的。结果表明,该方法足够稳健,能够克服两个测试案例的关键问题并提供可靠的转速估计。