## 1 Introduction  
With the development of modern industry, gears have become one of the most critical parts of modern industry. Gearboxes have been widely used in various machines due to their fixed transmission ratio, high transmission torque, and compact structure. The gearbox has become a variable speed transmission component for all kinds of machines. According to Nippon Steel Corporation [1], gear failures account for about 10.3% of the total number of machine failures. According to the statistics regarding gearboxes' failure parts, the failure of the gear itself accounted for the largest proportion of failures, about 60%; the gear drive is an important part of induced machine failure. Therefore, gearbox fault diagnosis research has important significance. Gearboxes are mechanical systems in which several components operate in a lubricated environment, and lubrication analysis, acoustic emission analysis, and vibration analysis can be applied to detect faults [2]. Among these, vibration signals have been widely considered. Many studies have proposed monitoring methods based on vibration signals. For example, [PERSON] et al. proposed a comprehensive method for detecting and classifying rotating bearing faults in machines, by combining permutation entropy (PE) with Flexible Analytic Wavelet Transform (FAWT) methods. Healthy FAWT decomposition signals faulty bearing systems under different operating conditions. In decomposition, the PE value is used to calculate the signal and then it is provided as a feature vector for a support vector machine (SVM) classifier for different types and fault sizes [3]. The present paper provides a novel approach for a Network Intrusion Detection System using machine learning and deep learning. Our approach uses two MLP (Multi-Layer Perceptron) models, one with three layers and the other with six layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and the testing time is reduced. The novelty of this paper lies in the choice and the combination of the models for network security [4]. Scholars have conducted in-depth research on the feature extraction and diagnosis methods of gearbox transmission faults based on vibration signals, and the current analysis methods based on the vibration signals of gearboxes mainly include Fourier transforms, empirical modal decomposition [5], ensemble empirical modal decomposition [6], wavelet packet decomposition [7], variational mode decomposition, and so on. These methods achieve, to some extent, the pre-processing of gearbox vibration signals and the classification and identification of transmission faults. Empirical modal decomposition can adaptively decompose vibration signals; however, the problem of modal aliasing is prominent, which makes the physical meaning of the missing decomposed modal components and the cause of the faults difficult to explain. Ensemble empirical modal decomposition is improved (based on empirical modal decomposition) by adding white noise signals, which suppresses the endpoint effect and modal aliasing to a certain extent, but its computational complexity increases. Wavelet packet decomposition solves the problem of insufficient extraction of high-frequency components of the signal based on a wavelet transform; however, its decomposition effect is very dependent on a manual wavelet base selection, which does not meet actual online diagnosis needs. Variational mode decomposition (VMD) solves the problem of modal aliasing, has a solid theoretical foundation, and can be used for better diagnosis. VMD can better restore the original signal and adaptively decompose it into a time series with different frequencies that are relatively smooth. Therefore, in this paper, the vibration signal is analyzed by variational mode decomposition, to decompose the modal components that contain the information of normal and wear faults of gear transmission systems.