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In the orbital environment, spacecraft face challenges such as scarce fault samples, varying operating conditions, and a strong reliance on accurate models and labeled data in traditional diagnostic methods. This paper systematically reviews transfer learning techniques for spacecraft fault diagnosis, highlights their recent advancements, and outlines future research trends. Transfer learning strategies are categorized into four types: instance-based, feature-based, model-based, and domain-adaptive. The principles,advantages, limitations, and representative applications of each strategy are analyzed, along with key enabling techniques such as importance weighting, adaptive batch normalization, parameter fine-tuning, and adversarial training. The review shows that transfer learning effectively mitigates issues of data insufficiency and distribution shift by enabling knowledge transfer from source to target domains. In particular, multi-source domain adaptation and adversarial domain adaptation significantly improve cross-condition diagnostic performance by enhancing model generalization and robustness. It is concluded that transfer learning provides a promising framework for intelligent spacecraft fault diagnosis. Future research should focus on source-free domain adaptation, multi-modal data fusion, semi-supervised transfer learning, and model interpretability, aiming to support practical deployment in real-world aerospace missions.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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