Fault Diagnosis of CNC Equipment Components Under Variable Operating Conditions Based on Fine-tune and DDC
Aiming at the problems of few fault diagnosis data samples of CNC equipment components in complex industrial environ-ment,difficulty in diagnosing faults under variable operating conditions and low accuracy,a fault diagnosis method based on model mi-gration was proposed.Continuous wavelet transform was used to preprocess the original vibration data under different operating condi-tions,and a 2D time-frequency dataset was established,which was divided into source domain and target domain.The source domain dataset and CNN were used for pre-training.Then,two transfer learning methods of Fine-tune and deep domain confusion(DDC)were introduced to improve the model.Finally,the fault diagnosis models based on Fine-tune and DDC were constructed.Taking the bearing and CNC milling cutter as example,the results show that Fine-tune and DDC can effectively improve the fault diagnosis accuracy of CNC equipment components,among which Fine-tune has strong generalization ability,while DDC takes less training time and performs better in complex environments.
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