DEC-NWD: an approach for open set domain adaptation in fault diagnosis

Domain adaptation provides a promising approach to cross-domain fault diagnosis of rotating machinery. While many current methods focus on scenarios where the source and target domains share identical label spaces, a prevalent situation inindustrial production involves the target domain being a subset of the source domain, known as partial domain adaptation (PDA). The main challenge in PDA is the label mismatches caused by outlier classes, making the alignment between domains particularly difficult. To this end, a dual-weighted adversarial network (DWAN) is proposed in this paper. Specifically, a bilateral class weighting strategy (BCWS) is developed, which can effectively suppress negative migration at the decision boundary while improving the robustness of adversarial training by applying bilateral weighting to both the source and target domains. Moreover, a collaborative framework is developed to facilitate positive migration. The constructed global perceptual module (GPM) is highly correlated with the improved adversarial loss function, which adaptively adjusts the input feature map according to the accuracy of the target domain classification and pays more attention to the domain-invariant features. Experimental results for two cases, namely across different operating conditions and across different rotating components, verify the effectiveness and superiority of the proposed method for the PDA problem.

Section: Introductionmentioning

confidence: 99%

A dual-weighted adversarial network for partial domain fault diagnosis of machinery

Ding,

Deng,

Deng

et al. 2024

A three-stage cross-domain intelligent fault diagnosis method for multiple new faults

Zhang,

Tang,

Ren

2024

Due to the randomness and complexity of mechanical faults, the new fault modes usually occur unexpectedly in the actual scenarios. In response to the challenges, a three-stage crossing domain intelligent fault diagnosis method is presented in article. Firstly, the partial domain alignment is achieved based on improved target weighted mechanism, and the outlier identifier is constructed to automatically separate the new fault classes. Then, the unsupervised learning model with silhouette coefficient are built to determine the number of new fault categories. Lastly, the simulation signals are further adopted to determine the specific fault categories. Sufficient experiments on the axial piston pump and public bearing datasets validate that the proposed method could predict the number of new fault categories and judge the specific fault categories. The results indicate that the proposed method outperforms the other methods and has promising practical application in fault diagnosis with multiple new faults.

Open-set fault diagnosis based on dynamic triple multivariate guided structural constraints

Wang,

Liu,

Gao

et al. 2024

Existing deep learning-based models for mechanical fault diagnosis perform well in identifying predefined faults, but these models substantially degrade in performance when they encounter unknown faults. Thus, it is crucial to investigate open-set fault diagnosis that can handle unknown faults more efficiently. Current methods for open-set fault diagnosis in machinery face challenges by the lack of hierarchical structure in feature representation and the overlapping regions of known and unknown sample distributions. To solve these problems, we propose a composite dual-branching dynamic triplet multivariate constrained (CDDTMC) model for mechanical open-set fault diagnosis. The CDDTMC framework consists of three main core modules: a feature extraction module, a structural constraint module and a fault diagnosis module. In the feature extraction module a composite two-branch network is designed to extract hierarchical feature representations from known samples. After extracting the sample features, it represents the samples with structural constraints using multivariate constraints based on bidirectional dynamic triplet loss to achieve discriminativeness and compactness. Determining the optimal decision boundary for each category based on the structural constraints and uses a distance-based diagnostic algorithm to identify fault diagnosis. We conducted experiments on two publicly available bearing datasets to validate the performance of the model. The results show that the model improves the Average Accuracy Classification (ACC) by 10.73% and 13.84%, respectively, compared to other comparative model.