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Broken Rotor Bar Fault Diagnosis for Induction Motors Using Power Spectral Density and Complex Continuous Wavelet Transform Methods

机译:基于功率谱密度和复连续小波变换的感应电动机转子断条故障诊断

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Induction motors are widely used in various industrial sectors, fault diagnosis of induction motors are critical to prevent equipment failure and production downtime. In this paper, a stator current signature analysis method is proposed for squirrel cage induction motors' broken rotor bar (BRB) fault diagnosis. Two different techniques are implemented: Power Spectral Density (PSD) based stator currents' amplitude spectrum analysis; and one dimensional Complex Continuous Wavelet Transform (CWT) based stator currents' time-scale spectrum analysis using Complex Morlet Wavelet (CMW). The performance of the two techniques are compared using experimental stator current data measured in a lab for a 0.25 HP induction motor. The stator current under healthy and faulty states of the motor were measured, the faults include one, two and three BRBs. For 2 and 3 BRB faults, the holes were drilled on the rotor bars 90 degree apart. Two loading conditions of the motor were used during the measurement, 30% and 85%. It is found that the CWT has better performance than the PSD estimates for the BRB fault detection.
机译:感应电动机广泛用于各个工业领域,感应电动机的故障诊断对于防止设备故障和生产停机至关重要。提出了一种定子电流签名分析方法,用于鼠笼感应电动机的转子断条故障诊断。实现了两种不同的技术:基于功率谱密度(PSD)的定子电流幅度谱分析;一维复杂连续小波变换(CWT)基于定子电流的时标频谱分析。使用实验室中针对0.25 HP感应电动机测得的实验定子电流数据,比较了这两种技术的性能。测量了电动机在健康状态和故障状态下的定子电流,故障包括一个,两个和三个BRB。对于2个和3个BRB故障,在转子棒上以90度的距离钻出孔。在测量过程中使用了两种电机负载条件,分别为30%和85%。发现对于WTB,CWT具有比PSD估计更好的性能。

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