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FPGA based on-line fault diagnostic of induction motors using electrical signature analysis

机译:使用电子签名分析的基于FPGA的异步电动机在线故障诊断

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摘要

Preventive maintenance is one of the main concerns in modern industry, in which early failure detection increases the lifecycle of machines. In this paper, electrical signature analysis is employed to indicate the development or existence of faults within the proposed system and this is achieved by embedding a real-time frequency analysis of the motor current. The term in the title electrical signature analysis basically refers to the motor current or voltage attributes are being used as a transducers to detect the changes in their spectrum in both the conditions; healthy and unhealthy. The algorithm used for analyzing the signals in frequency domain is done using Fast Fourier transform. In this work, we have focused on failure of bearing part of single phase induction motor and developed hardware for monitoring conditions (i.e. health of the motor) in run time. Because of the simplicity of this technique the mechanism of fault diagnosis is employed using an FPGA approach that offers re-configurability. This work can be very useful in industrial setup where there are 100 motors working together for some production lines. The findings show promising results which could lead to better reliability performance of the induction motor and lower maintenance costs.
机译:预防性维护是现代工业中的主要问题之一,在早期工业中,早期故障检测会延长机器的生命周期。在本文中,电气签名分析被用来指示所提出的系统中故障的发展或存在,这是通过嵌入电动机电流的实时频率分析来实现的。电气签名分析标题中的术语基本上是指电动机电流或电压属性被用作换能器,以检测两种情况下其频谱的变化;健康和不健康。用于分析频域信号的算法是使用快速傅立叶变换完成的。在这项工作中,我们专注于单相感应电动机的轴承部件故障,并开发了用于在运行时监视状态(即电动机的运行状况)的硬件。由于该技术的简单性,使用提供可重新配置性的FPGA方法来采用故障诊断机制。这项工作在工业设置中非常有用,在工业设置中,某些生产线需要100个电机一起工作。研究结果显示出令人鼓舞的结果,可以导致感应电动机具有更好的可靠性,并降低维护成本。

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