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基于三层小波包分解的陀螺仪故障诊断
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2009-06-22
贾飞小
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通过三层小波包分解将陀螺仪的输出信号进行分解，对分解得到的八个不同频段上的
节点进行特征提取，提取后的8 维特征向量作为神经网络的输入。对RBF 神经网络进行训练，训练后的神经网络进行故障诊断。对神经网络进行测试，经测试当系统输入向量存在故障时，系统可以准确的诊断出故障类型。
关键词: 小波包分解；小波神经网络；故障诊断；陀螺仪
Abstract: Sampling the status output of gyro, decompose the signal by three-layer
wavelet packet. Feature exertion after decomposition for eight nodes, then an
8-dimensional eigenvector is used as fault samples to train Radical Basis Function
(RBF) neural network. After the training process the network can detect a fault
on-line. After testing the method can detected the faults accurately.
Key words: wavelet package transform；wavelet neural network；fault diagnosis；
gyro
节点进行特征提取，提取后的8 维特征向量作为神经网络的输入。对RBF 神经网络进行训练，训练后的神经网络进行故障诊断。对神经网络进行测试，经测试当系统输入向量存在故障时，系统可以准确的诊断出故障类型。
关键词: 小波包分解；小波神经网络；故障诊断；陀螺仪
Abstract: Sampling the status output of gyro, decompose the signal by three-layer
wavelet packet. Feature exertion after decomposition for eight nodes, then an
8-dimensional eigenvector is used as fault samples to train Radical Basis Function
(RBF) neural network. After the training process the network can detect a fault
on-line. After testing the method can detected the faults accurately.
Key words: wavelet package transform；wavelet neural network；fault diagnosis；
gyro
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