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In order to improve the structural damage identification effect based on vibration signal, a structural damage identification method based on the combination of correlation function and convolutional neural network is proposed. Taking a railway steel girder bridge structure as an example, firstly, the signal-to-noise ratio of the vibration signal is improved by performing autocorrelation calculation on the vibration response of the structure, then the autocorrelation sample is used as the input of convolutional neural network, which can significantly improve the recognition accuracy. When the noise level in the vibration signal is higher, the improvement effect of the recognition accuracy of the autocorrelation sample as the convolutional neural network input is more obvious, and the autocorrelation operation has stronger noise immunity than that of the fast Fourier transform. The cross-correlation function is used to fuse the data of the multi-sensors arranged on the structure, then the fused signal is used as the input of the convolutional neural network. Under the premise of effective fusion of the data characteristics of the two sensors, the cross-correlation can double the dimension of the data set and reduce the number of parameters of the network operation, thereby reducing the time and improving the training efficiency, and the cross-correlation sample as the network input also has high recognition accuracy and strong noise immunity.
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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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