Multi-sensor signal fusion for tool wear condition monitoring using denoising transformer auto-encoder Resnet

Abstract

Multi-sensor signal fusion is commonly used in associate with the artificial intelligence model to monitor tool wears. However, AI models equipped with limited multi-sensor training samples still exist problems: 1) The limitation of training samples may cause the AI model failure due to the intrinsic wear features contaminated by heavy noises. 2) The selection of multi-sensor signals as training samples is a difficult task for the negative impact on the recognition accuracy using inappropriate sensor features. Therefore, this paper proposes a denoise transformer Auto-Encoder (DTAE) as pre-processor for the tool condition monitoring (TCM) classifiers. The reconstruction task of the DTAE allows the model to pay more attention to the intrinsic wear features and the appropriate selection of them from the multi-sensor signals during feature extraction. Moreover, the loss function for DTAE ResNet is formed by summing the reconstruction loss from DTAE with the classification loss from ResNet. Compared DTAE ResNet to stacked sparse auto-encoder network, deep stacked auto-encoder network, ResNet-18, VGG-16, and LSTM, experiments demonstrated that the present method would attain the highest classification accuracies for tool wear suitable for TCM. And a comparative experiment was conducted to verify the effectiveness of the DTAE preprocessor in improving the anti-noise performance of the classifier.