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。
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