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To cope with the difficulties of fault warning in current engineering practice, such as the challenges of constructing sensitive feature combinations, scarcity of complete fault samples, and inaccurate warning threshold settings, etc., a rolling bearing fault warning method based on Adversarial Autoencoder (AAE) and adaptive threshold was proposed. Firstly, the preprocessed normal sample spectrum data was utilized as AAE training data for autoencoder network and adversarial network training, and the autoencoder reconstruction error was calculated and the coding network was retained; Then, the low-dimensional features obeying the prior distribution was extracted layer by layer using the encoder, and the health indicator was constructed by combining the reconstruction error and similarity measure, and the probability density distribution of the health indicator was fitted based on the beta distribution to determine the threshold adaptively; Finally, the test data was processed by the same steps and compared with the threshold to discriminate abnormalities. The proposed method was verified by using two types of rolling bearing datasets, and the experimental results show that the proposed method has excellent fault warning performance and adaptability, and can realize early warning of weak fault.
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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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