# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 5 Conclusions  
In this paper, we propose a new fault diagnosis method for unbalanced data based on improved 1 DCNN and L2-SVM. Firstly, the ReLU activation function in the traditional CNN is replaced with Linear and SELU activation functions. Linear is used in the input layer to ensure that all minority class samples can enter the model for training to improve its recognition accuracy. SELU is used in the middle layer to overcome the disadvantage that ReLU is more fragile during the training process. Secondly, the four convolution pooling layers are used to extract the more abstract and deeper signal features implied in the input data, layer by layer, and then the extracted signal features are mapped to the class space through the full connection layer. In the class space, the cross-entropy loss is replaced by the L2-SVM loss, and a dynamic adjustment parameter is introduced to assign a lower misclassification cost to the majority class samples and more cost to the minority class samples. Finally, we add another modulation factor to reduce the weight of easy-to-classify samples and let the model focus on training a small number of hard-to-classify samples during the training process.  
Three datasets with different unbalanced degrees under different loads are constructed based on the CWRU public bearing dataset, and similar datasets under different speeds are constructed based on the lab-measured bearing dataset. All these datasets are used to check the validity of the presented method. The experiments prove that our method has higher recognition accuracy when dealing with unbalanced datasets. Then, further comparison experiments were carried out with six other commonly used methods. The results show that the proposed method has obvious advantages over traditional machine learning methods such as BPNN and SVM. The reason is that the proposed method does not need any manual feature extraction and selection process, and the extracted features have more comprehensive and deeper characteristics.  
Compared with R-CNN and S-CNN, which use the traditional single activation function, the proposed method also obtains certain advantages, which proves the effectiveness of the improvement of the activation function in this paper. Even compared with the commonly used unbalanced data fault identification methods, cost-sensitive CNN (CSCNN) and SMOTE-CNN, it still has certain advantages because this research designs two new parameters, namely, dynamic adjustment parameters and modulation factors. Dynamic adjustment parameters make the model improve the weight of the majority class samples during training, while modulation factors reduce the weight of more distinguishable samples. Under the joint action of the two designed parameters, the diagnostic accuracy of the indiscernible samples has been increased.  
In the future, transfer learning and multi-scale will be introduced to elevate the domain adaptability of the model. In addition, there are some possible misclassifications, so it is necessary to apply the proposed model to more environments, considering other datasets and integration methods to further optimize the model.  
Methodology, B.H. and J.L.; software, B.H. and T.H.; writing--original draft preparation, B.H.; writing--review and editing, J.L. and R.Z.; supervision, Y.X.; data curation, T.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (71861025 and 51675253), the National Key Research and Development Plan (Grant Number 2018 YFB1703105), and the Hongliu First-class Disciplines Development Program of Lanzhou University of Technology.  
Not applicable.  
Data Availability Statement: The data presented in this study are available upon request from the corresponding author.