An imbalanced data learning approach for tool wear monitoring based on data augmentation

During cutting operations, tool condition monitoring (TCM) is essential for maintaining safety and cost optimization, especially in the accelerated tool wear phase. Due to the safety constraints of the actual production environment and the tool's properties, the data for each wear stage is usually unbalanced, and these unbalances lead to difficulties in failure monitoring. To this end, a novel TCM method based on data augmentation is proposed, which uses generative adversarial networks (GANs) to generate valuable artificial samples for a few classes to balance the data distribution. Unlike the traditional GANs, the proposed Conditional Wasserstein GAN-Gradient Penalty (CWGAN-GP) avoids pattern collapse and training instability and simultaneously generates more realistic data and signal samples with labels for different wear states. To evaluate the quality of the generated data, an evaluation index is proposed to evaluate the generated data while further screening the samples to achieve effective oversampling. Finally, the continuous wavelet transform (CWT) is combined with the convolutional neural network (CNN) architecture of Inception-ResNet-v2 for TCM, and it is demonstrated that data augmentation can effectively improve the performance of training classification models for unbalanced data by comparing three classification methods with two data augmentation experiments, and the proposed method has a better monitoring performance.