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Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines

机译:基于短时傅里叶变换和支持向量机的故障信号分类新混合方法的实现

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Increasing the safety of a high-speed motor used in aerospace application is a critical issue. So an intelligent fault aware control methodology is highly research motivated area, which can effectively identify the early fault of a motor from its signal characteristics. The signal classification and the control strategy with a hybrid technique are proposed in this paper. This classifier can classify the original signal and the fault signal. The performance of the system is validated by applying the system to induction motor faults diagnosis. According to our experiments in BLDC motor controller results, the system has potential to serve as an intelligent fault diagnosis system in other hard real time system application. To make the system more robust we make the controller more adaptive that give the system response more reliable.
机译:提高用于航空航天应用的高速电动机的安全性是一个关键问题。因此,智能的故障感知控制方法是研究的重点领域,可以有效地从电动机的信号特征中识别电动机的早期故障。提出了一种基于混合技术的信号分类和控制策略。该分类器可以对原始信号和故障信号进行分类。通过将系统应用于感应电动机故障诊断,可以验证系统的性能。根据我们在BLDC电机控制器结果中的实验,该系统有潜力在其他硬实时系统应用中用作智能故障诊断系统。为了使系统更加健壮,我们使控制器更具适应性,从而使系统响应更加可靠。

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