By means of the wavelet analysis, non-steady signals are analyzed, the fault feature vectors of fault are successfully extracted and this effective method is employed to identify the fault pattern.
研究了小波分析在非平稳信号分析的实际应用,成功地通过小波分析提取故障信号的特征信息,为识别故障类型提供了有效的分析手段。
After wavelet analysis extracts feature vectors, the paper constructs corresponding neural network for fault identification, and designs the combination of wavelet and neural network approach.
本文针对小波分析之后提取的特征向量,构建了与之相适应的神经网络进行故障识别,并对小波和神经网络的结合方式进行了设计。
Pressure signal and vibration signal of valves are fused, and RBF neural network is used to diagnose fault by the information feature vectors constructed in this method.
该方法将气阀的压力信号和振动信号特征进行信息融合,再通过RBF神经网络进行故障诊断。
Pressure signal and vibration signal of valves are fused, and RBF neural network is used to diagnose fault by the information feature vectors constructed in this method.
该方法将气阀的压力信号和振动信号特征进行信息融合,再通过RBF神经网络进行故障诊断。
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