# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 1 Introduction
The above studies are mainly based on the conversion between one-dimensional vibration signals and two-dimensional images, applying CNN for fault diagnosis. However, valuable fault information is easily missed in the image conversion process, and the relationship between local and global features is also easily ignored. In addition, the signal conversion operation still depends on the experiences of experts, which may underuse the function of CNN's feature extraction and self-learning. Therefore, [PERSON] [29] proposed a multi-channel, multi-layer one-dimensional convolutional neural network (1 DCNN) classifier that can simultaneously process multiple channels of sensor data and improve the fault diagnosis performance of bearings. [PERSON] [30] proposed an early fault detection system based on 1 DCNN; it is provided with inherent adaptive adjustment and is directly applicable to the original vibration signal, bringing high detection efficiency. Recently, [PERSON] [31] proposed a bearing fault detection system based on 1 DCNN. The system combines feature extraction and fault classification and directly takes the original vibration signal as the input, which avoids the repetitive running of the feature extraction algorithm during each vibration data analysis and greatly reduces computational complexity. [PERSON] [32] proposed a new 1 DCNN by matching the characteristics of convolution kernels with the characteristics of the original signal. [PERSON] [33] proposed a new CNNEPDNN model by effectively fusing CNN and DNN and trained the local networks with different training datasets to extract different features of local areas so as to fuse features with different recognition abilities for fault diagnosis. [PERSON] [34] proposed a 1 DCNN based on a multi-scale convolution kernel. The front end of the network adopts a two-layer multi-scale convolution structure with convolution kernels of various dimensions to extract the features of different resolutions for the original vibration signal. Most recently, [PERSON] [35] proposed a multi-task 1 DCNN that uses the backbone network to learn the common characteristics needed by each task and successively deals with different tasks through multiple specific branches. Multi-task learning may fully utilize the obtained characteristic information so as to realize a better diagnostic result of the network. Although the above 1 DCNNs achieved good performance in diagnosis, the classification results were obtained on balanced datasets, ignoring the influence of unbalanced samples on diagnostic accuracy. In practice, the bearing operates normally most times, while faults are occasional. Therefore, in the data collected by the sensors, normal signals account for the vast majority, while the proportion of fault signals is small, which leads to the imbalance between normal signals and fault signals. In addition, the amount of data for different fault types is not equal [36]. The data imbalance will force CNN to bias the feature learning on the majority class samples and cause classification errors due to the underfitting of the minority class samples. For this problem, an improved 1 DCNN fault diagnosis method for unbalanced data is proposed in this paper. The main contributions are as follows:  
1. Because the ReLU activation function in traditional CNN will assign all the negative values of 0, this may bring about a significant decrease in the recognition accuracy of the minority class samples. Therefore, here, the ReLU activation function is modified by Linear and SELU. Linear is utilized in the input layer to ensure that all input minority samples can enter the training model. SELU is used on the middle layer; it gives a small negative slope rather than 0 to the negative value for better application to minority class samples under data imbalance. 2.