# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 1 Introduction  
During the era of Industry 4.0, artificial intelligence and machine learning are continually developing, and neural networks, as a part of artificial intelligence and machine learning, increasingly attract attention. The employment of neural networks to replace workers for machine fault diagnosis and monitoring has become a mainstream method, and many machines are equipped with gear systems on the transmission system. Due to long, continuous work and a harsh working environment, gear sets are very prone to wear, broken teeth, pitting and other damage. When the gearbox fails, it will have a significant effect on the performance of the machine. When the gearbox fails, the vibration signal will be changed. By analyzing the vibration signal, the characteristic signal related to the fault can be separated to determine the fault category of the mechanical system. The vibration signal generated by the gear contains important information about the development of the fault. Using signal processing technology, the key features related to faults can be clearly identified. The commonly used signal processing techniques include empirical mode decomposition (EMD) [1], cepstrum analysis, wavelet analysis [2] and the wavelet-based threshold denoising method [3]. [PERSON] et al. [4] put forward a fault diagnosis method for rotating machinery grounded in empirical mode decomposition. Theyimproved the decomposed intrinsic mode function (IMF) and proposed a combined mode function (CMF) to combine adjacent IMFs together to obtain a more accurate oscillation mode describing signal characteristics. [PERSON] et al. [5] introduced a new adaptive ensemble empirical mode decomposition (EEMD) method to address the issue of mode mixing during signal decomposition by improving the EEMD technique. The method overcame the drawback of mode mixing by adaptively selecting the sifting number during the decomposition process for signal analysis. However, the operation efficiency of EEMD [6] is low, and some noise remains after it is decomposed. To this end, [PERSON] et al. [7] first decomposed the original signal into six IMF components and residual component by using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and then expressed the time complicatedness of the IMF through permutation entropy to measure characteristics. [PERSON] et al. [8] introduced an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) energy entropy combined with a support vector machine (SVM), optimized by an artificial fish swarm algorithm for fault diagnosis. The ICEEMDAN energy entropy of different vibration signals was calculated to recognize the location of bearing faults and the accuracy of the model was improved. [PERSON] et al. [9] utilized multiscale permutation entropy (MPE) technology to extract characteristics from bearings and combined with an SVM to automate the fault diagnosis process. Its performance is superior to that of the approach derived from single-scale permutation entropy. [PERSON] et al. [10] conducted a comparative study on the fault diagnosis of rolling bearings by using methods such as deterministic/random signal separation, time frequency, and cyclical analysis; the results showed that the combination of cepstrum prewhitening and squared envelope spectrum and improved envelope spectrum had the highest accuracy. [PERSON] et al. [11] discussed the spectrum analysis of cyclostationary signals, pointing out the similarities, differences, and potential pitfalls between cyclic spectrum analysis and classical spectrum analysis. While the evolution of deep learning theory is ongoing, various models with strong data mining abilities have been extensively used in the realm of fault diagnosis. [PERSON] et al. [12] utilized a one-dimensional convolutional neural network (1 DCNN) for fault diagnosis of rolling bearings.