## 1 Introduction
Nevertheless, the above-mentioned methods require much domain expertise and prior knowledge and often rely on hand-crafted features. Often, these methods require a large amount of work in feature extraction. The use of frequency features of vibration signals for gear fault diagnosis has been common over the past decade. [PERSON] et al. [13] obtained the amplitude and frequency demodulation spectra by applying the Fourier transform to the amplitude envelope and the instantaneous frequency of the selected sensitive intrinsic mode functions. The planetary gearbox fault was detected based on the features shown in the demodulated spectrum. Although frequency domain features can be directly related to fault type and level, these features are usually abstract representations and require additional pre-processing [14]. By directly extracting the gear pitting fault signals in the time domain, the calculation cost and the time cost can be saved. [PERSON] et al. [15] used a backpropagation (BP) neural network to train the gears of four typical fault modes and obtained satisfactory results. The results showed that the BP neural network could effectively perform gear fault diagnosis. These methods can only extract the shallow features for gear fault diagnosis. There are limitations in mining deep features. Fortunately, with the development of deep learning, it is possible to extract fault features directly from the raw signals. Deep learning has been rapidly developed in recent decades [16]. [PERSON] et al. [17] integrated dictionary learning into a stacked autoencoder network for gear pitting fault diagnosis. They applied the sparse autoencoder algorithm to gear fault detection for the first time. [PERSON] et al. [18] presented a CNN-based deep learning method that automatically learned effective fault features directly from the raw vibrational signals, and classifies fault types in a single frame to provide a wind turbine gearbox diagnostic system based on end-to-end learning. Under 10 ten operating conditions, there were 2600 samples for each health condition, and each sample contained 2000 data points. [PERSON] et al. [19] used a convolutional neural network (CNN) to learn features directly from the frequency data of the vibrational signal. Feature learning using CNN can provide better results than manual feature extraction. [PERSON] et al. [20] presented a local feature-based Gated Recurrent Unit (GRU) network to predict machine conditions. A compact spectral data acquisition instrument was used for signal acquisition with a sampling frequency of 1024 Hz and a sampling window of 512 seconds. The accuracy of gear failure was 95.8%. [PERSON] et al. [21] presented a method of parallel training depth model, which can train different parts of it at different speeds. By splitting the deep neural network model and training on different devices at different speeds, it can speed up the whole training process. The training accuracy of this method was about 70%. [PERSON] et al. [22] used four classical deep neural networks to classify and identify fault conditions in the transmission. It was shown that the vibration signal usually contains abundant information for fault detection, control, and maintenance planning of rotating machinery. [PERSON] et al. [23] used a dual-tree complex wavelet transform to acquire the characteristics of multi-scale signals. The CNN was then used to automatically identify fault features from multi-scale signal features. This method can distinguish 4 kinds of gear faults, but the classification of these 4 kinds of faults is relatively easy. However, the detection of early gear pitting fault was not explained in their paper. Their experimental results of gear fault identification showed the feasibility and effectiveness of the presented method. In a nutshell, the deep learning method has been used in the diagnosis of gear pitting faults and has made certain progress. Vibration signals have been traditionally used for gear pitting fault diagnosis.