Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis

• URL: http://arxiv.org/abs/2509.09251v1
• Date: Thu, 11 Sep 2025 08:35:43 GMT
• Title: Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis
• Authors: Hanyang Wang, Yuxuan Yang, Hongjun Wang, Lihui Wang,
• Abstract summary: We propose a Multi-Attention Meta Transformer method for few-shot unsupervised rotating machinery fault diagnosis (MMT-FD)<n>This framework extracts potential fault representations from unlabeled data and demonstrates strong generalization capabilities.<n>The model is iteratively optimized using a small number of contrastive learning iterations, resulting in high efficiency.
• Score: 4.9825074884178955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The intelligent fault diagnosis of rotating mechanical equipment usually requires a large amount of labeled sample data. However, in practical industrial applications, acquiring enough data is both challenging and expensive in terms of time and cost. Moreover, different types of rotating mechanical equipment with different unique mechanical properties, require separate training of diagnostic models for each case. To address the challenges of limited fault samples and the lack of generalizability in prediction models for practical engineering applications, we propose a Multi-Attention Meta Transformer method for few-shot unsupervised rotating machinery fault diagnosis (MMT-FD). This framework extracts potential fault representations from unlabeled data and demonstrates strong generalization capabilities, making it suitable for diagnosing faults across various types of mechanical equipment. The MMT-FD framework integrates a time-frequency domain encoder and a meta-learning generalization model. The time-frequency domain encoder predicts status representations generated through random augmentations in the time-frequency domain. These enhanced data are then fed into a meta-learning network for classification and generalization training, followed by fine-tuning using a limited amount of labeled data. The model is iteratively optimized using a small number of contrastive learning iterations, resulting in high efficiency. To validate the framework, we conducted experiments on a bearing fault dataset and rotor test bench data. The results demonstrate that the MMT-FD model achieves 99\% fault diagnosis accuracy with only 1\% of labeled sample data, exhibiting robust generalization capabilities.

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