Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

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.

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HF-MSCN: a high frequency-multiscale cascade network for bearing fault diagnosis

Abduelhadi,

Liang,

Cao

et al. 2024

Meas. Sci. Technol.

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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.

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Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

Cited by 4 publications

References 41 publications

Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided

HF-MSCN: a high frequency-multiscale cascade network for bearing fault diagnosis

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