# Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis

arXiv: https://arxiv.org/abs/2509.09251 · HTML: https://mlsanity.com/paper/2509.09251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 14 of 14)

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- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (2019)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [Towards Task Sampler Learning for Meta-Learning](https://mlsanity.com/paper/2307.08924/) (2023)
- [SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection](https://mlsanity.com/paper/2207.00589/) (2022)
- [Meta-Auxiliary Learning for Micro-Expression Recognition](https://mlsanity.com/paper/2404.12024/) (2024)
- [Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning](https://mlsanity.com/paper/2408.09676/) (2024)
- [Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection](https://mlsanity.com/paper/2507.13061/) (2025)

## Abstract (arXiv)

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.