Few-Shot Learning for Intelligent Fault Diagnosis

Few‐shot learning has emerged as a vital approach in intelligent fault diagnosis, addressing the perennial challenge of scarce labelled data in industrial monitoring. Traditional deep learning techniques demand extensive datasets to achieve reliable performance, yet many complex systems—such as rotating machinery, wind turbines and aerospace components—offer only limited fault samples under real operating conditions. Few‐shot paradigms equip models to generalise from only a handful of examples, rapidly adapting to novel fault types and varying environments. Central strategies include metric‐based methods, which learn similarity measures between known and unknown states, and meta‐learning schemes that train across multiple diagnostic tasks to foster swift adaptation. Complementary techniques such as data augmentation, transfer learning and domain adaptation further bolster robustness. The result is a suite of lightweight, generalisable models capable of accurate fault classification and prognosis with minimal retraining. Such capabilities promise to reduce maintenance costs, prevent unplanned downtime and enhance safety across global industrial sectors.

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Few-Shot Learning for Intelligent Fault Diagnosis publication trend

The graph below shows the total number of articles in few-shot learning for intelligent fault diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Few‐shot learning: A paradigm in machine learning that enables models to learn new concepts or fault categories from only a few labelled examples.

Support set: The small collection of labelled samples used to guide the few‐shot model in recognising novel fault types.

Query set: The set of unlabelled samples on which the few‐shot model is evaluated for fault classification.

Siamese neural network: An architecture that learns similarity metrics by comparing pairs of samples through shared weights.

Prototypical network: A metric‐based approach that represents each fault class by the mean of its support embeddings to classify queries.

Model-agnostic meta-learning: A meta-learning strategy that trains models across tasks to optimise for rapid adaptation to new faults with minimal data.

References

1. Small data challenges for intelligent prognostics and health management: a review. Artificial Intelligence Review (2024).
2. Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network. Sensors (2023).
3. A Model-Agnostic Meta-Baseline Method for Few-Shot Fault Diagnosis of Wind Turbines. Sensors (2022).