电机振动分析“,

M. Saravanan, G. K. Rajini
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引用次数: 0

摘要

近年来,拥有电机的工业主要集中在机器监控上,涉及到多种方法。其中一些方法是化学、热和振动监测。这些方法需要高精度的传感器,但在这种情况下,振动监测不需要高精度的传感器,这是本工作的重点。介绍了一种基于振动信号识别机器年龄的新方法,并利用信号处理技术对结果进行了提取。通常旧机器产生巨大的振动,但在新机器振动较少观察。我们的算法和技术将很容易识别机器类型。本文将离散小波变换(DWT)和离散小波包变换(DWPT)用于新旧机器的识别。利用这些变换域技术,计算了全局阈值、阈值系数和熵等统计特征。从结果中,可以方便地识别机器的年龄和寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vibration Analysis of Electrical Machine
Recently industries that possess electrical machines mainly focuses on machine monitoring which involves many methods. Some of the methods are chemical, thermal and vibration monitoring. These methods require high accuracy sensors but in this case of vibration monitoring high accuracy sensors are not required which is emphasized in this work. The new approach is introduced to recognize the machine age based on vibration signal and the results are extracted by using signal processing techniques. Generally old machine creates huge vibration but in new machine vibrations are less observed. Our algorithm and techniques will easily recognize the machine type. In this paper, DWT (Discrete Wavelet Transform) and DWPT (Discrete Wavelet Packet Transform) used for recognizing old and new machines. Using these transform domain techniques, global threshold, threshold coefficient and statistical features like (entropy) were computed. From the results, it is convenient to recognize the machine's age and its lifetime.
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