通過(guò)三層小波包分解將陀螺儀的輸出信號(hào)進(jìn)行分解，對(duì)分解得到的八個(gè)不同頻段上的
節(jié)點(diǎn)進(jìn)行特征提取，提取后的8 維特征向量作為神經(jīng)網(wǎng)絡(luò)的輸入。對(duì)RBF 神經(jīng)網(wǎng)絡(luò)進(jìn)行訓(xùn)練，訓(xùn)練后的神經(jīng)網(wǎng)絡(luò)進(jìn)行故障診斷。對(duì)神經(jīng)網(wǎng)絡(luò)進(jìn)行測(cè)試，經(jīng)測(cè)試當(dāng)系統(tǒng)輸入向量存在故障時(shí)，系統(tǒng)可以準(zhǔn)確的診斷出故障類(lèi)型。
關(guān)鍵詞: 小波包分解；小波神經(jīng)網(wǎng)絡(luò)；故障診斷；陀螺儀
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