# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 1 Introduction
[25] constructed a deep shrinkage residual network with added soft thresholds to extract feature information from bearing vibration data under noise redundancy, completing fault diagnosis by applying marginal distribution constraints to input features. [PERSON] et al. [26] transferred the distribution of source domain sample features to the distribution area of target domain sample features, and used sufficient source domain samples to train the recognition model, improving the classification accuracy of the model in the target domain. [PERSON] et al. [27] proposed a joint mean difference matching algorithm to establish a common subspace, thereby completing fault diagnosis for industrial processes. The premise of the above literature research is that the target domain (industrial environment) has a large and balanced sample of fault data. However, the practical utility of the data is weak, leading to insufficient target domain data to train high-accuracy models in industrial production. And traditional adversarial transfer networks include only one feature extractor, which is responsible for mapping both source domain and target domain sample features, significantly compromising the performance of the feature extractor under such demands. Therefore, this paper proposes a Collaborative Domain Adversarial Network for unlabeled bearing fault diagnosis, which utilizes prior knowledge from the source domain samples (laboratory manual fault samples) and unlabeled target domain samples (fault samples in industrial environments) for adaptive learning, achieving unlabeled bearing intelligent fault diagnosis. Domain adversarial adaptation involves establishing a model to use source domain information for related target domain fault identification. This study sets up two feature extractors for different mapping tasks of the source and target domains, and the main contributions are summarized as follows:1. This paper proposes a Collaborative Domain Adversarial Network (CDAN) model with two types of feature extractors for unlabeled bearing fault identification, which can transfer the fault characteristics of laboratory artificial fault samples to the distribution area of real fault sample characteristics, and fully train the fault diagnosis model using the rich transferred fault characteristics. 2. A strategy of using multi-kernel clustering to pseudo-label target domain samples has been proposed, which solves the problem of failure diagnosis in mechanical engineering due to lack of labels in actual faults
3. The two types of feature extractors can accurately map the source domain features and target domain features, reducing the difference in sample feature distribution and lowering the negative transfer rate of sample transfer learning, thereby improving the accuracy of unlabeled bearing fault diagnosis. The rest of this article is organized as follows. Section 2 describes the theoretical background. Section 3 introduces the model framework, forward propagation, and parameter optimization process of CDAN. Section 4 analyzes and verifies the effectiveness of CDAN by experiments. Section 5 concludes this article.