Few-Shot Bearing Fault Diagnosis with Multimodal LLMs and Prototypical Networks
Few-Shot Bearing Fault Diagnosis with Multimodal LLMs and Prototypical Networks
Initial release of the few-shot bearing fault diagnosis framework using multimodal LLMs and prototypical networks. Features: Support for multiple MLLMs: GPT-4o, GPT-5.1, Claude 4.5 Haiku/Sonnet, LLaVA-1.5-7B Prototypical Networks baseline with ResNet-50 and Swin Transformer V2-T 1-shot, 5-shot, and 10-shot evaluation Automated metrics computation with confidence intervals CWT image preprocessing for vibration signal analysis Requirements: Python 3.8+ OpenAI/Anthropic API keys for cloud models User-provided bearing vibration datasets (CWT images) See README.md for installation and usage instructions.
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prototypical networks, condition monitoring, multimodal large language models, few-shot learning, bearing fault diagnosis, vibration analysis, continuous wavelet transform
prototypical networks, condition monitoring, multimodal large language models, few-shot learning, bearing fault diagnosis, vibration analysis, continuous wavelet transform
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