# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 1 Introduction  
As a necessary component of most mechanical equipment, gears are widely used in the system of engineering machinery, aerospace, and ship vehicles for its advantage of power transmission. Due to the complex structure and poor working condition of the transmission system, gears can be easily damaged and cause the machine to break down, which contributes to the increased failure probability of whole system. Therefore, it is in urgent demand to seek effective gear fault diagnosis methods that can effectively detect and identify fault feature information of gears. In the past decade, a vibration signal analysis method has become one of the most used and effective methods for gear fault pattern recognition and fault localization due to the abundant information and clear physical meaning in the mechanical vibration signal [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. For example, [PERSON] et al. [1] proposed a method of planetary gear fault diagnosis via feature extraction based on multi-central frequencies and vibration signal frequency spectrum. [PERSON] et al. [2] proposed a feature extraction and gear fault diagnosis method based on vibrational mode decomposition, singularvalue decomposition, and convolutional neural network (CNN). [PERSON] et al. [3] used the method of permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) to make gear fault diagnosis in a planetary gearbox. However, in many practical situations, the installation of vibration sensors is usually inconvenient due to constraints imposed by working conditions or even the equipment themselves. Moreover, vibration signals are not easy to be measured in high temperature, high humidity, high corrosion, and toxic and harmful environments, so the vibration signal analysis method for gear fault diagnosis is limited by its requirement of contacted measuring. Considering that, it is in urgent need to develop acoustic-based diagnosis (ABD) methods to overcome this disadvantage via non-contact measurements. In recent years, a few studies have explored various acoustic-based fault diagnosis techniques [13; 14; 15]. [PERSON] et al. [16] compared the advantages and disadvantages of non-contact, air-coupled ultrasonic sensors and contact piezoelectric ultrasonic sensors in health monitoring of rotating machinery. [PERSON] et al. [17] used non-contact microphone sensors to acquire acoustic signals of rotating machinery for predicting residual life. [PERSON] et al. [18] designed a new acoustic emission sensor technique to detect the fault mode of the bearing. With the development of array measurement technology, some researchers also applied this technology in acoustic-based diagnosis. By combining near-field acoustic holography (NAH) with spatial distribution feature extraction, [PERSON] et al. [19; 20; 21] proposed a gearbox fault diagnosis method that is based on the spatial distribution features of a sound field. The NAH technology was used to reconstruct the sound field near the sound source, and then texture features were extracted from the acoustic images to reflect the distribution characteristics of the sound field. Finally, combining with the support vector machine-based pattern classification, the fault diagnosis of a gearbox was realized. [PERSON] et al. [22] proposed a rolling bearing fault diagnosis method, which is based on NAH and a grey-level gradient co-occurrence matrix (GLGCM). The NAH technology was also used to reconstruct sound files of different bearing conditions, then GLGCM features were extracted from the acoustic images and support vector machines were used for rolling bearing fault diagnosis. These methods overcame the limitations of traditional single channel measurement-based ABD methods and solved the local diagnosis problem in ABD.