# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 1 Introduction  
Fault diagnosis is crucial given the widespread use of sophisticated technologies in industrial machinery and equipment [1; 2; 3; 4; 5; 6; 7]. In production, various equipment faults, such as wear and tear of moving parts, strong corrosion, strong vibration, etc., will result in the equipment's effectiveness and lifespan degrading with time [8; 9; 10; 11; 12; 13; 14]. The occurrence of failures and a substantial financial loss can be avoided if the equipment faults are promptly identified and fixed [15]. According to [PERSON], approximately 30% of machinery failures can be attributed to rolling bearing faults [16]. Therefore, it is essential to analyze bearing fault signals obtained from rotating machinery and then conduct effective techniques and methods to avoid these failures [6; 17; 18; 19; 20; 21]. Methods for diagnosing bearing faults rely on analyzing vibration signal data [1]. Numerous scholars have studied the cross-domain defect diagnostic problem based on transfer learning in the last few years. Originally, two approaches, transfer component analysis (TCA) and maximum mean difference (MMD), were utilized for cross-domain fault diagnosis [22]. However, the issues with unstable data and unpredictable fault characteristics in the complexity and variety of the natural working state cannot be overcome with these methods. To handle this, [PERSON] et al. suggested a cross-domain feature extraction based on TCA in 2017 [23]. The approach still has poor performance in the accuracy of fault detection. In 2019, [PERSON] et al. proposed a semi-supervised transfer component analysis (SSTCA) with variational mode decomposition (VMD) and various feature structures to increase the accuracy in predicting bearing conditions with compound faults or under limited training data scenarios [24]. To improve fault detection accuracy further, the algorithms based on deep learning methods have been widely studied and applied due totheir self-adaptive learning characteristic [25; 26; 27; 28; 29; 30; 31]. In detail, a convolutional neural network with automated hyper-parameter tuning based on Bayesian optimization was presented by [PERSON] in 2021 [32]. [PERSON] has suggested a Frequency-Domain Fusing Convolutional Neural Network (FFCNN) as a representation adaptation-based strategy for filtering inputs from various frequency bands and fusing them into new input signals by using a frequency-domain fusing layer [33]. Other issues with radial internal clearance and the meshing force of a gear system have been considered recently. To deal with the problem of understanding the influence of radial internal clearance on the dynamics of a rolling-element bearing, [PERSON] utilized a nonlinear mathematical model and carried out experimental validation. The findings reveal characteristic dynamical behaviors within specific clearance ranges, offering insights into optimal clearance values [34]. Additionally, to explore the influence of the inconsistent distribution of mesh force on the transmission performance of the gear system, [PERSON] proposed an 18-degrees-of-freedom bending-torsion-swing-coupled dynamic model of a pair of involute spur gears for clearly describing the coupling relationship between the nonuniformly distributed meshing force, shaft bending deformation, and dynamic center distance [35]. With the introduction of adversarial-based transfer learning methods, adversarial-based learning is a technique in machine learning that improves model robustness by training against adversarial examples--carefully crafted inputs designed to deceive the model. By incorporating adversarial examples during training, models become more resilient and better equipped to handle real-world challenges, improving generalization capabilities and defense against adversarial attacks. The approaches generated by generative adversarial networks (GAN) have been presented. In 2021, [PERSON] proposed a mixed adversarial network (MANN) based on a residual learning network for cross-domain-based fault diagnosis of machinery equipment [36].