Reading
Code repository for reproducing the paper 'Multimodal data-enabled large model for machine fault diagnosis towards intelligent operation and maintenance', focusing on single-dataset experiments using the HUSTbearing dataset.
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Code repository for reproducing the paper 'Multimodal data-enabled large model for machine fault diagnosis towards intelligent operation and maintenance', focusing on single-dataset experiments using the HUSTbearing dataset.
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LLMFuxian is an academic paper reproduction project aimed at reproducing the methods in the paper 'Multimodal data-enabled large model for machine fault diagnosis towards intelligent operation and maintenance'. This project focuses on the field of intelligent operation and maintenance of industrial equipment, using multimodal large model technology to achieve automatic diagnosis of mechanical faults.
Currently, it has mainly completed the reproduction of single-dataset experiments using the HUSTbearing dataset from the paper, and does not yet include the gear dataset, FRC/CAME external baselines, or complete ablation experiments.
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In modern industrial production, the health status of rotating machinery (such as bearings and gearboxes) directly affects production efficiency and safety. Traditional fault diagnosis methods face the following challenges:
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The LMM-FD (Large Multimodal Model for Fault Diagnosis) method proposed in the paper combines the knowledge understanding ability of large language models with time series analysis, and enhances entity representation through knowledge graphs to achieve zero-shot compound fault diagnosis capability.
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The project has completed the following core functions:
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.xls files20Hz / 40Hz / 60HzX/Y/Z three axesSection 08
A carefully designed training/test division strategy is adopted:
This design simulates real industrial scenarios—compound fault samples are scarce, and the model needs to perform zero-shot inference on unseen compound fault types.
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