# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 1 Introduction  
In recent years, with the increasing proportion of clean energy in total energy consumption, the wind power industry has been rapidly developing, and wind power has become one of the most widely used energy sources [1, 2]. Wind turbines are typically installed in high-altitude outdoor environments, where they operate continuously under dynamic loads and face complex and varied operating conditions. Consequently, the gearbox in the entire power transmission chain has a relatively high probability of failure [3, 4, 5, 6, 7]. The gearbox in wind turbines is relatively complex in structure. If a component within it fails and is not promptly detected and replaced, it can lead to downtime of the turbine, increasing operational costs [8, 9]. Therefore, monitoring the operational status of the gearbox, detecting any anomalies in a timely manner, and diagnosing the type of failure have significant engineering value [10]. In practical gearbox fault diagnosis in wind turbines, most studies have focused on analyzing vibration signals, which have proven to be effective. However, research on faultdiagnosis based on sound signals is relatively limited, and most of it has been conducted in laboratory settings. For example, [PERSON] et al. developed a gearbox fault diagnosis scheme based on near-field acoustic holography and acoustic field spatial distribution characteristics, and achieved satisfactory diagnostic results [11]. [PERSON] et al. utilized the fusion of acoustic and vibration signals using deep random forests to explore gearbox fault diagnosis under different operating conditions [12]. [PERSON] et al. proposed a method for diagnosing roller faults using audio wavelet packet decomposition and convolutional neural networks, significantly improving the efficiency of diagnosing faults in sand-carrying roller drums [13]. [PERSON] et al. proposed an improved method of local mean decomposition which separates the frequency-modulated and amplitude-modulated components in the sound signal, enabling composite fault diagnosis of gearboxes [14]. [PERSON] proposed an online fault diagnosis system for belt conveyor rollers based on stacked sparse autoencoders, convolutional neural networks, and spectral clustering algorithms. This system collects audio data through sensors, extracts fault features through analysis, and then performs diagnosis [15]. [PERSON] et al. utilized sound signals for fault diagnosis and employed Fourier decomposition for fault diagnosis under conditions of limited sample data, demonstrating superior diagnostic performance compared to vibration signals under experimental conditions [16]. The aforementioned scholars analyzed the sound signals of industrial equipment, diagnosed mechanical faults, and achieved fruitful research results. However, due to technological limitations, the detection accuracy can be further improved. With the development of artificial intelligence, deep learning models have achieved higher accuracy in recognizing and classifying sounds and images. Considering the advantages of strong fault sensitivity, easy acquisition, and non-invasive measurement of sound compared to vibration signals, this study preprocesses wind turbine sound data using Mel spectrograms. Subsequently, a deep learning model is employed to classify the data and diagnose the types of faults present. Using advanced signal processing techniques and deep learning classification models is of great significance for improving the accuracy and efficiency of gear fault diagnosis. Compared with the shallow fault features of traditional time-domain signals and frequency-domain signals, the Mel spectrogram generates a two-dimensional feature map that contains both time and frequency domains, simulating the human ear's perception of sound. It better handles low-frequency sound signals, reduces the dimensionality of frequency-domain signals from the original frequency to logarithmic Mel frequency, decreases the redundant information of features, and improves the efficiency of signal processing.