# Deep Residual Network Combined with Transfer Learning Based Fault Diagnosis for Rolling Bearing
## 1 Introduction  
Rolling bearing is one of the most frequently used parts in mechanical equipment; it will affect the production efficiency of large machinery, thus affecting the benefits of companies and the safety of employees. It is significant to find out the degree and type of faults in rolling bearings timely and effectively. Traditional bearing fault diagnosis methods based on machine learning mainly include feature extraction and fault classification. Feature extraction is an essential step in fault diagnosis. [PERSON] et al. [1] proposed a novel feature extraction method based on hierarchical analysis. [PERSON] et al. [2] proposed a noise reduction method based on ensemble empirical mode decomposition (EEMD). [PERSON] et al. [3] obtained the optimal features of EEMD by using some feature selection algorithms. [PERSON] et al. [4] based on EEMD designed an effective frequency cut-off criteria and extracted the unknown fault features. Traditional bearing fault diagnosis technology based on machine learning has many requirements for technicians, and there is the risk of information loss [5]. Among the various types of research for rolling bearing fault diagnosis, the most practical and mature method is mechanical signal analysis because it can identify bearing faults by focusing on the intrinsic frequencies with tiny vibrations. Numerous scholars have concluded from their analyses that it is feasible to use current motor frequencies to detect bearing faults [6, 7]. However, there are two difficulties in using motor current signals for fault diagnosis: One is that there is no clear signal band to observe bearing faults effectively, and the other is that signal strengths' temporal characteristics as the deterioration progresses are unclear. [PERSON] et al. [8] diagnosed bearing degradation by monitoring both the bearing's natural frequency and its rotational frequency and demonstrated the feasibility of using motor current signals for fault diagnosis through relevant motor experiments. [PERSON] et al. [9] presented a current signal-based bearing fault diagnosis using a DeepLearning (DL) algorithm CNN and a novel decision-level IF technique. The proposed bearing fault diagnosis method has suitable practical applications with cost-effective and relatively high diagnostic accuracy. [PERSON] et al. [10] evaluated the detection of load machine bearing outer ring faults by efficiently extracting features of faulty components from the stator current using continuous wavelet transform. [PERSON] et al. [11] reported a relationship between vibration and current frequencies caused by faults in the bearings while using vibration monitoring and motor current signature analysis (MCSA) techniques. The above papers found by summarizing that the small amplitude (i.e., low energy) of the fault frequencies extracted from the faulty bearing stator currents can easily be mistaken as noise coming from the system, resulting in the poor diagnosis of bearing faults based on current signals. Regarding the research on rolling bearing fault diagnosis, more and more scholars have started to experiment with other signals in recent years, such as stray magnetic flux signals, thermal imaging images, acoustic emission signals, etc. [PERSON] et al. [12] used statistical analysis of stray flux signals around the motor to conduct fault diagnosis of bearings. [PERSON] et al. [13] used an advanced algorithm based on a combination of the MUSIC method and artificial neural networks (ANN), which enables the automatic calculation of fault severity indicators when using stray flux signals. [PERSON] et al. [14] used a thermal imager to acquire data on rolling bearings and used the bearing temperature to distinguish the type of failure. Although more economical than the traditional signal acquisition method, experiments showed that the approach was more applicable to the outer race of the bearing. [PERSON] et al.