Secondary EWT-CNN Tool Wear Monitoring Based on Vibroacoustic Fusion
In order to realize the fault monitoring of the working state of the tool during the machining process,a method based on collaborative filtering and fusion is proposed to analyze the feature correlation between the vibration signal and the sound signal of the working tool and then fuse the data layer,and the obtained acoustic-vibroacoustic fusion signal is denoising and reconstructed after the second empirical wavelet transform(EWT),and finally the reconstructed signal is enhanced and sent to CNN to realize fea-ture extraction and tool fault identification.Through the fault identification experiment of twist drill bits of different fault types,the method has a high recognition rate of 98.96%for drill bits of different fault types under the comparison of sound,vibration,vibroacoustic fusion signals and different signal denoising recon-struction methods.The superiority of the proposed method in tool fault detection is verified.
NETL
NSTL
万方数据
维普
