# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 1 Introduction  
The gearbox is an important part of the mechanical transmission. The failure of the gear in the gearbox will lead to the damage of the system, which will be paralyzed in serious cases. Therefore, gear fault monitoring and diagnosis is an important means to prevent system damage. In the operation of the gearbox, the vibration signal generated by the gear failure is weak and difficult to monitor. Therefore, it is especially important to monitor the vibration signal of gear faults. Vibration signal monitoring is an important means of gear fault diagnosis [1]. In the past, the sensor signal transmission in industrial monitoring was transmitted by wire, but with the rise of the Internet of things (IoT), it has become an important technology in the monitoring system [2]. For offshore wind turbines, the gear is usually used for wind turbines, but due to the limitation of its internal structure, there is not enough space for sensors to monitor signals for fault diagnosis, so an inductive thermal imaging method is proposed to detect gear faults by Gao [3]. In the aspect of gear fault monitoring based on data, a signal processing method is used to process gear fault signal. Traditional signal processing methods have been widely applied to gear fault diagnosis, such as time-frequency analysis [4], wavelet analysis [5, 6], Hibert-Huang transform (HHT), empirical mode decomposition (EMD) [7], and local mode decomposition (LMD) [8]. However, the wavelet transform is based on the analysis of the specified wavelet base. Once the specified wavelet base is specified,the decomposed mode is fixed. It is better to use different wavelet bases for the analysis of different signals to achieve the best processing effect. EMD and LMD are prone to endpoint effect and mode mixing during the decomposition process. Therefore, [PERSON] [9] proposed a variational mode decomposition to solve the problems caused by EMD and LMD in the decomposition process. [PERSON] [10] used variational mode decomposition (VMD) to extract various fault features of the gearbox under strong noise environment, and compared with the ensemble empirical mode decomposition (EEMD) decomposition results, it shows that the algorithm can effectively improve the signal-to-noise ratio of the signal. [PERSON] [11] proposed a fault diagnosis method based on VMD and generalized composite multi-scale dynamic entropy (GCMSDE) to identify different health conditions of planetary gearboxes. [PERSON] [12] uses VMD to decompose the planetary gearbox vibration signal into several intrinsic mode functions (IMFs), and performs Fourier transform on the amplitude envelope and instantaneous frequency of the sensitive IMFs to obtain the amplitude and frequency demodulation spectrum. The planetary gearbox faults have been detected based on demodulation and have been successfully identified on all three gears (sun gear, planetary gear, and ring gear). [PERSON] [13] used the improved VMD algorithm to diagnose the gearbox and compared it with EEMD to verify the effectiveness of the proposed method. [PERSON] [14] proposes an improved VMD linked wavelet denoising method, which can suppress high frequency narrowband noise and normal noise in electromagnetic acoustic transducer (EMAT) signal, and this method can retain defect information. In recent years, researchers have studied a large number of fault classification algorithms. Among them, the gear fault classification algorithms mainly include support vector machine (SVM) [15], artificial neural network (ANN) [16], and deep learning [17]. [PERSON] [18] proposed a gearbox fault diagnosis model based on wavelet support vector machine. The results show that it has stronger generalization ability and higher diagnostic accuracy than artificial neural network and support vector machine with random extraction parameters. [PERSON] [19] used different optimization methods to optimize SVM parameters and used continuous wavelet transform (CWT) and wavelet packet transform (WPT) for feature extraction.