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An improved algorithm for detection of rotor faults in squirrel cage induction motors based on a new fault indicator

机译:基于新故障指示器的鼠笼式感应电动机转子故障的改进算法

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In this paper an improved fault detection algorithm is proposed to ameliorate the reliability of the rotor fault detection task. The proposed algorithm uses the Motor Current Signature Analysis (MCSA); it is based on monitoring the Relative Harmonic Indexes (RHI) as a new fault indicator. The most sensitive harmonics to the occurrence of rotor bar faults contribute to the calculation process of the RHI, which represents its main advantage compared to the classical fault indicators. The proposed algorithm requires only the stator current signal as input. For each data acquisition, it estimates the slip, identifies the frequencies and the amplitudes of the searched harmonics then it computes the RHI. After that, the algorithm normalises and classifies the RHI. The obtained results are displayed on the monitor screen of the personal computer. For any data acquisition, the proposed algorithm allows the user to know the motor state, the fault severity and the slip. A lot of experimental tests, carried out on a 3kW squirrel cage induction motor, confirm the effectiveness of the proposed algorithm to detect rotor bar fault under different operation conditions even at very low loads.
机译:本文提出了一种改进的故障检测算法,以提高转子故障检测任务的可靠性。所提出的算法使用了电动机电流签名分析(MCSA)。它基于监视相对谐波指数(RHI)作为新的故障指示器。对转子条故障的发生最敏感的谐波有助于RHI的计算过程,这代表了RHI与传统故障指示器相比的主要优势。所提出的算法仅需要定子电流信号作为输入。对于每个数据采集,它估计转差率,识别搜索到的谐波的频率和幅度,然后计算RHI。之后,该算法对RHI进行归一化和分类。获得的结果显示在个人计算机的监视器屏幕上。对于任何数据采集,所提出的算法都允许用户了解电动机状态,故障严重性和打滑。在3kW鼠笼式感应电动机上进行的大量实验测试证实了该算法在即使在非常低的负载下也可在不同的运行条件下检测转子棒故障的有效性。

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