A Sensors Location Considered Piston Pump Fault Diagnosis Model With Bagging Based Convolutional Neural Network
Aiming at the problems of unstable vibration data and low diagnostic accuracy in the fault diagnosis of piston pumps,a fault diagnosis model of sensors location considered-Bagging-convolution neural network(SLC-B-CNN)is proposed.Firstly,the two-dimensional vibration time-frequency information after short time Fourier transform is matched with the sensors location information encoded by one-hot to construct the dataset.Then,the dual-input hybrid CNN model is designed as the base classifier.And finally the dataset is put into the SLC-B-CNN model that is aggregated by simple averaging.The proposed SLC-B-CNN model is verified on the experimental piston pump dataset,with an accuracy rate of 92％on the test set and an average recall rate of 89％for various faults.The performance is better than the CNN model and the random forest model.
NETL
NSTL
万方数据
