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A novel healthy state monitoring method for offshore booster station platforms is proposed to enhance the damage detection capabilities under complex operating conditions. Using a deep learning framework based on memory unit autoencoders, the method magnifies fault-relevant features via denoising and angular domain resampling high-frequency vibration data from offshore boosting stations. The model employs a deep convolutional neural network to learn historical data patterns, constructs a hidden state memory bank, and achieves sparse matching between sample encoded features and the memory bank. Finally, a Gaussian mixture probability model is employed to model the generated membership scores to assess the health status of the booster station. A case study of the offshore booster station in Rudong, Jiangsu, validates the approach, achieving an anomaly recall rate and accuracy of over 98%, outperforming other comparison algorithms.
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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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