基于神经网络的旋转机械故障诊断研究

一般对特定的基于多层感知的故障诊断问题，很难确定神经网络的结构，在分析了多层感知器对故障的识别和诊断能力后，采用由小到大和由大一小的方法确定神经网络隐层数与隐层单元数。

FAULT DIAGNOSIS FOR ROTATING MACHINERY BASED ON NEURAL NETWORKS

Generally, it is difficult to determine in advance a suitable network structure when a multi-layer perceptron neural networks is used for a special fault diagnosis problem. For this reason, the fault diagnosis and pattern recognizing ability of multi-layer perceptron was analyzed, the method of increasing or decreasing the numbers of hidden layer and hidden unit was adopted to determining the structure(including the numbers of hidden layer and hidden unit) of the neural networks. The fault diagnosis method based on this appreach for rotating machinery was studied, and the intelligent fault diagnolsis system was developed. The system consist of networks learning, fault diagnosis, data management and result inquiring. The knowledge sub-chunk was adopted and used in it; system has friend interface, powerful interchange and reliable diagnosis results. This system was used in fault diagnosis of the blower, it shows that this system is convenient to manipulate with precise result and powerful intelligence, and in some case the diagnosis can automatically be carried out. It is proved that this method is an effective approach to build fault diagnosis system for the rotating machinery.