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Addressing challenges in complex structural health monitoring arising from heterogeneous sensor node sampling, feature drift, and limited model generalization ability in distributed vibration signals, this study constructs a distributed vibration signal with augmented generation (DVSAG) dataset. It utilizes cross-diffusion for adaptive sampling while preserving the spatiotemporal correlation of the original signal, combines the frequency domain to unify input dimensions, and enhances inputs by calculating residuals using fault-free reference signals. A fault diagnosis network with a convolutional block attention module (CBAM) is designed to extract multi-scale features from distributed vibration signals. These features are converted into word embeddings, combined with user questions, and input into a distributed vibration signal large language model (DVSLLM). Finally, a feature alignment and semantic mapping framework is used to achieve fine-grained interaction from vibration signals to natural language. Experiments show that the proposed method effectively improves fault diagnosis accuracy and model generalization ability under multiple operating conditions, providing reliable support for multi-task decision-making in complex structural health monitoring.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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