Accurate Diagnosis of Rolling Bearing Based on Wavelet Packet and RBF Neural Networks
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摘要: 針對滾動軸承故障精密診斷的需要,采用小波包分析方法提取了滾動軸承故障的特征信號,通過小波包分析將高頻信號分解到8個頻帶中,以頻帶能量作為識別故障的特征向量,應用RBF徑向基神經網絡建立了從特征向量到故障模式之間的映射,現場采集的數據分析表明,采用小波包和神經網絡相結合的方法可以比較準確地識別滾動軸承的故障。Abstract: The accurate diagnosis of rolling bearing was studied. The wavelet packet analysis was used to abstract the characteristic of signals. The signals were decomposed into eight frequency bands and the information in the high band was used as a characteristic vector. RBF neural networks were used to realize the map between the feature and diagnosis. The analysis of data sampled form a workshop testified correctness of the method proposed.
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Key words:
- wavelet packet /
- RBF neural network /
- rolling bearing /
- diagnosis
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