多属性卷积神经网络及其在轴承故障诊断中的应用

he existing methods of bearing diagnosis have some disadvantages: The conventional method has complex mathematical calculation and poor diagnosis effect. It generally only diagnoses the fault location and irrespective of the load and the fault size. The existing convolutional neural network method use the traditional convolution neural network. A network can only output a property and can not simultaneously diagnose multiple properties. In order to simultaneously diagnose the fault location, fault size and load, for the first time put forward a multi-attributes convolution neural network (MACNN) and applied to the bearing fault diagnosis. The multi-attribute convolution neural network is trained using one-dimensional vibration signal training . The advantages lies in overcoming the shortcomings of the traditional method: the diagnosis result of any combination of the fault attributes can be obtained, the network parameters are less, the method is simple, the generalization ability is strong and the accuracy rate is high. A series of tests have been carried out using the bearing data of Case Western Reserve University. The results show that the proposed method can accurately diagnose several properties of bearing faults with high accuracy and good generalization ability.