# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
Affected by noise interference and variable loads, deep learning fault diagnosis methods perform unstablely in extracting fault features [16], and subsequently, the accuracy decreases and the generalization deteriorates, which eventually makes fault diagnosis challenging. Therefore, based on such considerations, some scholars have studied deep learning methods of response. [PERSON] et al. [17] used kernels of different sizes in the first part of the model to construct a multi-scale 1 DCNN that can capture fault features at different resolutions and enable fault diagnosis and simulated a noisy environment for diagnosis. [PERSON] et al. [18] proposed a domain-adaptive rolling bearing fault diagnosis model, called WDCNN, which has the significant advantage of resisting noise interference from vibration signals by using a wider convolutional kernel size in the first layer of the CNN and can be applied in the case of load variations. [PERSON] et al. [19] proposed a multi-task 1 DCNN, to improve fault diagnosis performance using two auxiliary tasks: speed identification task (SIT) and load identification task (LIT). [PERSON] [20] et al. proposed a CNN with training interference (TICNN), which achieves higher accuracy in noise and can be tested under different load conditions. These methods are designed to cope with the effects of realistic environments for fault diagnosis, but it is still room for improvement in these methods. What has been demonstrated is that CNNs are sensitive to other information about the real environment, such as noise and changing load conditions [21]. It can be improved by using one of its variants, the residual network, similar to what [PERSON] [22; 23] did, which focuses on improving the noise-resistant performance of CNN by increasing the number of layers with the help of residual connections, verifies that this technique is efficient when applied to fault diagnosis, unfortunately, however, they do not further explore the effect of variable load. Furthermore, using novel activation functions or attention mechanisms can help CNN to obtain better performance [24]. Such as in [25; 26; 27; 12], more or less, they seek or design suitable novel techniques to apply to their methods and enhance the performance for fault diagnosis. In a general overview, we found three problems with the vibration signal-based fault diagnosis method:  
First, traditional machine learning applications for fault diagnosis emphasize the use of signal processing to extract important features and then input them into a shallow fault classifier. This relies heavily on expert experience. Second, deep learning fault diagnosis methods do not emphasize expert experience but still need to further consider the impact of a realistic environment on it, such as noise and variable load. Third, CNN, as an excellent representative of deep learning, is unsatisfactory when applied to fault diagnosis considering realistic environments, and it is necessary to adopt techniques such as using residual connections to deepen the network and adding attention mechanisms to counteract useless information. Hence, we designed a coping method based on CBAM_ResNet and ACON activation functions. The main contributes of this paper are:  
1. A novel CNN named CBAM_ResNet is proposed and the ACON activation function is used in it. Our method does not rely on expert experience for fault diagnosis, it achieves 100% diagnosis of the experimental data. 2. The effects of realistic environments such as noise and load variations on our method are considered, and compared with other publicly available fault diagnosis methods in the same consideration, our method performs better. 3. By employing two optimization techniques: embedding the CBAM attention mechanism into the residual block and introducing the ACON activation function, the performance of fault diagnosis is improved, which maintains high diagnostic accuracy under noise interference and has the ability to generalize to cope with load variations.