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A rotor fault intelligence diagnosis system based on virtual instrument

机译:基于虚拟仪器的转子故障智能诊断系统

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In order to get the rotor fault characteristic component in stator current, an advanced correlation algorithm is presented in this paper. Because of faster and multi-channel ADC subsystem, S3C2410 ARM is chosen as the core of stator current acquisition to meet the need of spectrum analysis. By programming on LabWindows/CVI — a virtual instrument development tool, the fault information can be separated from stator current signal. Then make the spectrum analysis to the residual signal, the broken bar fault can be diagnosed easily. The results of experiment indicate that a reliable and portable system can be realized to meet the purpose of testing and diagnosing the faults of induction motors in-situation, and method is feasible and effective.
机译:为了使转子故障特性分量在定子电流中,本文提出了一种高级的相关算法。由于更快,多通道ADC子系统,选择S3C2410 ARM作为定子电流采集的核心以满足频谱分析的需要。通过对LabWindows / CVI进行编程 - 虚拟仪器开发工具,故障信息可以与定子电流信号分开。然后将频谱分析进行剩余信号,可以容易地诊断断线故障。实验结果表明,可以实现可靠和便携式系统以满足测试和诊断感应电动机故障的目的,方法是可行和有效的。

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