# Deep Residual Network Combined with Transfer Learning Based Fault Diagnosis for Rolling Bearing
## 1 Introduction
[15] proposed an emerging two-dimensional discrete wavelet transform (2D-DWT) based infrared thermography method to diagnose different bearing faults in induction motors. [PERSON] et al. [16] used acoustic emission (AE) signals containing bearing health information. They converted AE into 2D spectrograms by Constant Q-Transform (CQT) and then used CNN to infer the bearing status. [PERSON] et al. [17] used Fast Fourier Transforms (FFT) to preprocess AE signals and constructed a low-speed rolling bearing fault diagnosis model based on AE signals and subspace embedded feature distribution alignment. They concluded experimentally that AE signals outperformed vibration signals when rolling bearings were operating at low speeds. The diagnosis of rolling bearing defects is mainly studied using vibration signal features. Nowadays, the application of deep learning based on 2D-image data to diagnosis methods has gained more attention; it solves the uncertainty problem of artificial feature extraction and promotes the automation of fault diagnosis. [PERSON] et al. [18] produced research based on the Deep Belief Networks (DBN) and using vibration images to diagnose faults of the rotor system. [PERSON] et al. [19] converted the vibration signal into a 2D form and analyzed the identified flaws of the bearing through Convolutional Neural Networks (CNN) based on vibration images. [PERSON] et al. [20] combined transfer learning with CNN to use 2D time-frequency image sets for bearing fault diagnosis and confirmed RGB images contained more information than grayscale images. [PERSON] et al. [21] proposed a 2-level hierarchical diagnosis network to classify rotating machinery faults with the time-frequency images. [PERSON] et al. [22] generated Short-time Fourier, Wavelet Transform, and Hilbert-Huang transform maps and proved the Wavelet Time-frequency image has the highest accuracy. The results of previous research show that the Wavelet Time-frequency transform is more suitable for the time-frequency transform of RGB images. Besides, the Wavelet Time-frequency image can reflect the details of the signal, so it is ideal for fault classification [23]. When using deep learning for fault diagnosis of rolling bearings, CNN models have limitations because they require amounts of labeled data for training. Nevertheless, fault samples are sometimes challenging to meet this condition [24]. In this case, the classifier model is at risk of overfitting and poor generalization, which transfer learning (TL) can solve. [PERSON] et al. [25] used TL to deal with the problems of data imbalance and different distributions. [PERSON] et al. [26] proposed a method that combines CNN and TL for fault diagnosis. [PERSON] et al. [27] suggested a fault detection and diagnosis method based on TL to overcome the fault data rareness and no label issues. [PERSON] et al. [28] proposed a novel fault diagnosis method for deep convolutional domain adversarial TL. Many studies have shown that TL can effectively improve the situation of the model being limited by data and solve the problems of low accuracy of classifier caused by insufficient samples and changes in data distribution [29, 30]. The contribution of this paper is to propose a fault diagnosis framework with a high diagnostic rate. The framework uses transfer learning to improve the accuracy of the diagnostic network and converts 1D vibration data to 2D RGB image data before inputting it into the classification model to enhance the stability of the features. After pre-training the ResNet34 network, the time-frequency image set is fed into the ResNet34 network to learn the specific features of the fault data. Comparative experiments are conducted on the CWRU dataset and a laboratory dataset to compare ResNet34-TL with four other models (ResNet34, MobileNet-TL, MobileNet, and CNN) by the loss function value of the training set, the accuracy of the test set, and the accuracy of the validation set to diagnose the ResNet34-TL framework for detecting rolling bearing faults.