liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism
基于注意力机制的少量样本故障诊断 pytorch
This project helps operations engineers and maintenance professionals quickly identify faults in machinery, even when they have very little historical data on specific failure modes. It takes vibration or other 1D sensor signals from equipment and outputs a diagnosis of potential issues, making it easier to predict and prevent costly breakdowns. This is ideal for industrial environments with complex machinery.
278 stars. No commits in the last 6 months.
Use this if you need to diagnose machine faults with high accuracy but are limited by a small amount of training data for various fault types.
Not ideal if you already have a large, well-labeled dataset for all possible fault conditions, as simpler models might suffice.
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Python
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Jun 26, 2025
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