# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 1 Introduction  
Rolling bearings are widely used in many industrial scenarios such as aerospace, wind power generation, high-speed rail, steam turbines, nuclear power plants, etc. As the core supporting and gyrating parts of rotating machinery, their health condition is also vital to the security of mechanical equipment. However, rolling bearings easily suffer from mechanical faults due to impact loading, thermal fatigue, and mechanical wear [1]. According to statistics, 45-55% of the faults of rotating machinery come from the failure of rolling bearings [2]. Therefore, it is important to make more effort to monitor the fault diagnosis of mechanical equipment. For this reason, many findings on this issue have been published in recent decades. [PERSON] [3] reviewed the research progress and prospect of support vector machine (SVM) in fault diagnosis. [PERSON] [4] summarized the latest research progress on empirical mode decomposition (EMD) targeted at fault diagnosis. [PERSON] [5] systematically reviewed more than 20 major time-frequency analysis methods since 1990 and pointed out their basic principles, advantages, disadvantages, and their application in fault diagnosis. [PERSON] [6] identified, summarized, analyzed, and explained the main literature published from 2000 to 2017 on bearing fault diagnosis. In summary, although great achievements have been made, the following problems still exist [7; 8]:  
1. Traditional fault diagnosis methods mostly use manual approaches for feature extraction and pattern recognition, which rely on tedious diagnoses of specific data by experts and technicians and cannot meet the needs of industrial monitoring of Big Data;Most of the extracted features are shallow features, and the generalization performance may be constrained when facing complex classification problems. Deep learning [9; 10], which is a typical representative of artificial neural networks (ANNs) [11; 12], has made a breakthrough in research in many fields such as natural language processing [13], computer vision [14], image processing [15], instrument inspection [16], speech recognition [17], oil and gas storage testing [18], etc., and has motivated many experts to introduce deep learning into mechanical health monitoring [19; 20; 21; 22]. Nowadays, the research on fault diagnosis based on deep learning mainly focuses on four network models: convolutional neural network (CNN), stacked auto encoder (SAE), recurrent neural network (RNN), and deep belief network (DBN). Compared with SAE, RNN, and DBN, CNN may achieve superior results in feature learning and generalization for time series data because of its unique convolution structure, weight sharing, and sparse links [23]. [PERSON] [24] developed a new CNN for fault characteristic learning straight from frequency vibration signals, and the validity of this network was proven by PHM2009 gearbox challenge data and a planetary gearbox test bench. [PERSON] [25] proposed a new CNN based on LeNet-5 for fault diagnosis. It converts the original vibration signal into a two-dimensional image and then extracts the converted image features to eliminate the influence of handmade features. [PERSON] [26] proposed an asynchronous motor fault diagnosis technique by means of Q1 DCNN. The one-dimensional time-domain data are transformed into a two-dimensional gray image by a specific reconstruction method; the experiments proved more precise classification effects than the traditional machine learning method. [PERSON] [27] used discrete wavelet transform to convert vibration signals into two-dimensional signals containing time-domain information, then transformed low-level features into high-level features through CNN, and finally realized fault classification by using the softmax function. [PERSON] [28] converted one-dimensional vibration signals into two-dimensional vibration images and then classified this conversion result by CNN so as to realize the recognition of bearing faults.