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Advanced diagnosis of broken bar fault in induction machines by using Discrete Wavelet Transform under time-varying condition

机译:通过在时变条件下使用离散小波变换的离散小波变换,在感应机器中进行高级诊断

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The diagnosis of induction machine faults is commonly carried out by means of Motor Current Signature Analysis (MCSA), i.e., by classical spectrum analysis of the input currents. Specifically in case of broken bars, the amplitude of the left sideband component of a phase current is monitored in order to sense its signature. However MCSA has some drawbacks that are still under investigation. The main concern is that an efficient frequency transformation cannot be made under speed-varying condition, since slip and speed vary and so does the left sideband frequency. In this paper, an advanced use of the Discrete Wavelet Transform (DWT) is proposed to overcome the limitation of the classical approaches based on Fourier Analysis (FA). Experimental and simulation results show the validity of the developed approach, leading to an effective diagnosis method for broken bars in induction machines.
机译:感应机故障的诊断通常通过电动机电流签名分析(MCSA),即,通过对输入电流的经典频谱分析来执行。具体地,在残破条的情况下,监测相电流的左边带分量的幅度以便感测其签名。然而,MCSA有一些仍在调查的缺点。主要问题是,在速度变化条件下不能进行高效的频率变换,因为滑动和速度变化而且左侧带频率也是如此。本文提出了一种离散小波变换(DWT)的高级使用,以克服基于傅立叶分析(FA)的经典方法的限制。实验和仿真结果表明了开发方法的有效性,导致感应机器中断杆的有效诊断方法。

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