谐小波模糊神经网络应用于旋转机械的故障诊断

Fault Diagnosis of Rotating Machinery Based on Harmonic Wavelet Fuzzy Neural Networks

根据旋转机械复杂的故障特点,提出了结合谐小波分析、模糊理论和神经网络形成的谐小波模糊神经网络方法,并将其应用于旋转机械的故障诊断,实现了模糊故障诊断。通过计算机实现了全部算法。仿真和试验的结果表明:谐小波模糊神经网络在处理多故障耦合的情况时优势明显,故障诊断正确率高,证明该方法行之有效,为旋转机械的故障诊断提供了理论支持和新方法。

Since faults of rotating machinery appear in a complicated manner, a method called the harmonic wavelet fuzzy neural network method , which is a combination of harmonic wavelet analysis, fuzzy theory and neural networks is being presented. It has been applied to fuzzy fault diagnosis of rotating machinery with the whole computational process done by a computer. Results of simulation and tests show, that this method has its advantage in dealing with multi-coupled fault situations and is featured by a high probability of accuracy, which not only proves the method to be effective , but also provides a theoretical basis and a new way for fault diagnosis of rotating machinery. Figs 2, tables 3 and refs 7.