A Few-Shot Fault Diagnosis Algorithm for Rotating Machinery Based on Time-Frequency Dual-Stream Mamba and Joint Metric Learning
Experimental results show that the proposed few-shot fault diagnosis method consistently outperforms comparison methods under different rotational speeds, training sample scales, and 8-way 1-shot/5-shot tasks, validating its effectiveness and robustness for few-shot rotating machinery fault diagnosis.