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In response to the challenge of quantitatively diagnosing corrosion damage thickness within pipelines，a quantitative imaging method for pipeline corrosion damage using ultrasonic guided waves is proposed. Firstly，based on the frequency domain finite difference method，a numerical model for multi-path helical propagation of guided waves in pipes is established，enabling rapid calculation of guided wave reception signals when thickness map is known. Secondly，by calculating the received signals in the presence of randomly distributed damage，a database comprising 3 500 samples of damage signals is generated through iteratively running the numerical model. Subsequently，a one-dimensional convolutional neural network imaging model is constructed. The model is trained using the generated database to establish a mapping relationship between thickness maps and reception signals，and inputting the reception signals into the imaging model yields corresponding thickness maps. Finally，the feasibility of the proposed method is experimentally validated. The mean square error between experimental imaging results and actual values is 8.6048×10-4，the correlation coefficient is 0.711 6，and the imaging model runtime is 0.538 5 seconds. The results indicate that the proposed method can achieve quantitative imaging of corrosion damage thickness within pipelines with high imaging efficiency.
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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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