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For the challenge of fault identification under limited labeled data in engineering applications,a novel adversarial transfer network with class aggregation-guided (ATN-CA) is proposed for few-shot condition diagnosis of bearings. The ATN-CA can focus on the discrepancy features of bearings by the proposed local discrepancy feature representation, avoiding that the features extracted by a single neural network may omit important fault information. Moreover, the further proposed class aggregation-guided strategy uses the semantic information of signals to guide the dynamic adaptation of marginal and conditional distributions of source and target data, which shortens the distribution distance of the same category in different domains, thus completing the transfer diagnosis. By comparing with other current methods on the artificial and real bearing fault datasets,results show this proposed method has the highest test precision and the smallest accuracy deviation in bearing transfer diagnosis.
For the challenge of fault identification under limited labeled data in engineering applications,a novel adversarial transfer network with class aggregation-guided (ATN-CA) is proposed for few-shot condition diagnosis of bearings. The ATN-CA can focus on the discrepancy features of bearings by the proposed local discrepancy feature representation, avoiding that the features extracted by a single neural network may omit important fault information. Moreover, the further proposed class aggregation-guided strategy uses the semantic information of signals to guide the dynamic adaptation of marginal and conditional distributions of source and target data, which shortens the distribution distance of the same category in different domains, thus completing the transfer diagnosis. By comparing with other current methods on the artificial and real bearing fault datasets,results show this proposed method has the highest test precision and the smallest accuracy deviation in bearing transfer diagnosis.
In the field of data-driven fault diagnosis (FD), deep learning methods have proven their excellent performance, especially when dealing with complex signals from rotating equipment such as bearings. However, fault features in vibration signals are often mixed with noise features and distributed at different frequency scales, posing challenges for effective feature extraction. In order to solve this problem, this paper proposes a high frequency-multiscale cascade network (HF-MSCN), which enhances the noise suppression and feature learning capability of the model by combining a high-frequency convolutional block (HFCB) with a multi-scale cascade block (MSCB). HFCB effectively suppresses high-frequency noise through wide convolutional layers and self-attention mechanisms while still retaining essential high-frequency fault signals. MSCB enhances the interaction between convolutional layers at different scales by cascading the layers at different scales and strengthens the model’s ability to capture subtle fault features, especially when processing periodic fault pulse signals. Finally, we investigate the internal functioning of the network using time–frequency analysis methods in signal processing to improve the interpretability of deep learning methods in FD applications and further verify the enhanced effect of HFCB and MSCB on feature extraction. We validate the effectiveness of HF-MSCN on the case western reserve university dataset as well as a self-constructed bearing composite fault dataset, and the experimental results demonstrate that the network exceeds the performance of six state-of-the-art fault diagnostic methods in high-noise environments.
As the primary driving equipment in industrial, accurate fault diagnosis and condition monitoring of induction motor is crucial for ensuring operational safety. This paper focuses on the bearing faults of induction motors, which have a substantial impact on both the mechanical and electromagnetic systems of the motors. However, in diagnostic tasks, we are faced with the challenges of multi-source, multi-modal data, significant influence from environmental noise, and minimal differentiation between fault data. This paper proposed a novel cross-modal vector fusion fault diagnosis and classification model (CNN-ELMNet), which includes a Cross-Modal Vector Fusion Network (VF) based on D-S evidence theory, feature extraction layer (FE) and classification layer (CL). Specifically, the VF prioritizes the integration of diagnostic results from individual vibration signals or stator current signals within convolutional neural networks with the features of the input implicit vectors as decision-making evidence, followed by weighted vector fusion through D-S evidence theory at the decision level. The FE focuses on retaining the convolutional, pooling, and fully connected layers of the convolutional network and freezing the final fully connected layer, thus preserving training parameters and fully utilizing the network's powerful feature extraction capabilities. The CL includes an Extreme Learning Machine optimized for random hyperparameters using the SAO algorithm, which offers rapid convergence and high classification recognition rates. The CNN-ELMNet model combines a convolutional network with an Extreme Learning Machine optimized by the SAO algorithm, which not only preserves the model's feature extraction capability but also enhances the convergence speed and classification recognition rate of the model. Experimental results on real datasets demonstrate that the proposed model exhibits strong stability, generalization, and high accuracy in fault diagnosis, achieving an accuracy rate of 99.29% and 98.75%. This provides a more feasible solution for the bearing fault diagnosis of induction motors and holds promising prospects for practical applications.
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