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Condition Monitoring of Induction Motors Using Wavelet Based Analysis of Vibration Signals

机译:基于小波的振动信号分析感应电动机的状态监测

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Condition monitoring of machines has its roots in the human ECG analysis for detecting cardiac arrhythmias. Condition monitoring in industry is desirable for increasing machinery availability, reducing consequential damage, and improving the operational efficiency. This is very significant inindustries that use heavy duty machines for various processes. A monstrous three-phase AC induction motor to drive a city water supply pump or very big, high power motors used in mills, huge generators forgenerating power in hydel plants etc. depict a few of them. Also, for safety and economic considerations, there is a need to monitor the behavior of motors working in critical production processes as well. This paper demonstrates how the condition of an induction motor can be monitored by the analysis of the acoustic signal that represents the non-stationary vibration data. The analysis has been done using various signal processing algorithms and a robust fault detection scheme has been developed using the PSD (Power Spectral Density) concept in Wavelet Decomposition.
机译:机器的情况监测在人体ECG分析中有其根源,用于检测心律失常。行业中的情况监测是为了提高机械可用性,降低后续损坏,提高运营效率。这是一种非常重要的内行业,用于各种过程的重型机器。一种巨大的三相交流感应电动机,用于驱动轧机的城市供水泵或非常大的高动力电机,巨大的发电机在水轮工厂等中进行电力。描绘了其中一些。此外,对于安全和经济的考虑,还需要监测在关键生产过程中工作的电机的行为。本文演示了如何通过分析代表非静止振动数据的声学信号来监测感应电动机的条件。使用各种信号处理算法进行了分析,并且使用了小波分解中的PSD(功率谱密度)概念开发了鲁棒故障检测方案。

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