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Research and Application of Intelligent Diagnosis Method of Mechanical Fault Based on Transformer Vibration and Noise and BP Neural Network

机译:基于变压器振动和噪声和BP神经网络的机械故障智能诊断方法的研究与应用

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In this paper, a fault diagnosis method based on the transformer vibration noise and BP neural network is proposed. The transformer vibration noise signal is obtained by the vibration noise detection system, and the eigenvalue is calculated by FFT. The eigenvalue is used as the input of the trained BP neural network and the type of transformer fault is predicted. The validity of the method is verified by the diagnosis of typical state test of 6 kinds of transformers. This method makes full use of the vibration and noise signal of transformer, and realizes the diagnosis of transformer live fault by BP neural network algorithm. It greatly improves the fault diagnosis rate of the transformer. It greatly improves the fault diagnosis rate of the transformer and provides an effective way for the transformer live inspection.
机译:本文提出了一种基于变压器振动噪声和BP神经网络的故障诊断方法。 通过振动噪声检测系统获得变压器振动噪声信号,通过FFT计算特征值。 特征值用作训练的BP神经网络的输入,预测变压器故障的类型。 通过诊断6种变压器的典型状态测试验证了该方法的有效性。 该方法充分利用了变压器的振动和噪声信号,并通过BP神经网络算法实现了变压器实时故障的诊断。 它大大提高了变压器的故障诊断速率。 它大大提高了变压器的故障诊断速率,为变压器实时检查提供了有效的方法。

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