# Unsupervised Method Based on Adversarial Domain Adaptation for Bearing Fault Diagnosis
## 1 Introduction
This method uses the adversarial learning strategy to decrease marginal and conditional distribution discrepancies. In addition, [PERSON] has suggested an adversarial adaptation network based on classifier discrepancy (AACD) for diagnosing faults in different machines when the source and target domains have distinct data distributions [37]. This model conducts adversarial training to extract features from separable classes and invariant domains for fault diagnosis using two structures, a standard feature extractor and two task-based classifiers [37]. Furthermore, another adversarial-based unsupervised algorithm is the domain adversarial neural network (DANN), proposed for domain adaptation in 2016, which enables the learning of features from unlabeled input in some types of neural networks to conduct cross-data set classification [38]. The feature extractor in this network is a critical component that learns to extract transferable and relevant features from raw input data. It learns domain-invariant representations by minimizing domain discrepancies, enabling the effective transfer of learned features across different domains. According to the literature analysis on adversarial-based unsupervised algorithms above, the DANN feature extractor structure faces two key challenges:  
* _Overfitting problem._ A model's capacity is determined by its ability to fit data. A larger capacity means stronger data fitting but does not guarantee better generalization. The book _Deep Learning_[39] mentions that increasing model capacity reduces training error but can increase generalization error if it is too high for the problem's complexity. A widely held belief is that more parameters increase a model's fitting capacity by adding depth to deep-learning models. However, excessive fitting capacity leads to overfitting. To address this, a bottleneck residual block with a \"jump link\" is proposed for achieving identity mapping [40]. * _Low computational efficiency._ A model's computational efficiency greatly impacts network convergence. DANN is a technique used for domain adaptation. It employs a feature extractor and domain classifier to enable the transfer of learning between different domains. However, the DANN feature extractor's standard residual block with two convolutional layers has many parameters and operations, impeding computational efficiency. It lacks channel reduction before costly convolutions, hinderingthe model's ability to process data efficiently. Additionally, using \(3\times 3\) filters instead of \(1\times 1\) filters in the bottleneck residual block adds computational complexity, further increasing the computational requirements of the model. Considering the literature study as mentioned, our two main contributions are as follows:  
1. An improved feature extractor based on a bottleneck residual block is proposed. This bottleneck residual block utilizes two \(1\times 1\) convolutional filters and one \(3\times 3\) convolutional filter to replace the two original \(3\times 3\) convolutional filters. This structure can help the feature extractor extract transferable and robust features within a limited time to achieve better accuracy when handling complex and large-scale data. 2. The transferability of each model to conduct binary classification on bearing data sets is compared. Several models are utilized for comparisons, such as the baseline model, the DASN model, DANN with standard residual block, and MCD with residual block. The final results show that DANN and MCD, with this improved feature extractor, have achieved higher accuracy than other models. The outcomes of this research can theoretically help the operation of intelligent manufacturing systems safely and dependably. The objective of this paper is to address two critical challenges in deep learning models: overfitting and computational efficiency. Overfitting can be a significant issue when dealing with large amounts of data, hindering accurate data classification in the target domain.