Wheel balance plays an important role in vehicle safety. The existing inspection method for wheel balance mainly relies on the off-machine measurement technique, which is time-and manpower-consuming as the worldwide requirement of the automated production system gradually increases. However, the multi-unbalance causes are difficult to identify due to complex machine structures; and the low signal-noise-ratio between wheel and machine vibration makes traditional handcrafted features difficult to detect wheel unbalance. To overcome these two challenges, this paper proposes a Dynamic-Balancing-Inspection (DBI) scheme which integrates steps of data collection, data preprocessing, and ensemble average of Convolution Neural Network (CNN) based models with well-Tailored filters and activation functions, to automatically uncover critical information from frequency data and provide the on-machine and real-Time total inspection for the wheel balance. The application of the wheel balance from a practical CNC-machine is adopted to illustrate the performance of the DBI approach.
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