Compound fault diagnosis for diesel engine based on multi-channel convolutional neural network
Aiming at the problem of low diagnosis accuracy of compound faults,a diesel engine multi-fault simulation experiment was carried out in which a multi-channel two-dimensional convolutional neural network model improved by AlexNet was constructed and one-dimensional vibration signals were converted into two-dimensional time-frequency graphs by short-time Fourier transform,which was imported into the constructed model for training,so that fault diagnosis with adaptive feature ex-traction was realized.Comparison of the diagnosis results indicates that the accuracy of the single channel convolutional neural network can be higher only when the measurement point is set close to the fault source and otherwise,the accuracy is significantly reduced and thus the diagnosis accuracy of the compound fault is low;that the diagnosis accuracy of the multi-channel convolutional neural net-work is improved,i.e.,the accuracy of compound fault diagnosis can be improved by 11.4％.
NETL
NSTL
万方数据
