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To address the issues of low accuracy in rolling bearing life prediction and the difficulty of constructing health indicators, a bearing remaining life prediction model based on ASFF (adaptively spatial feature fusion) and AAKR (auto associative kernel regression) combined with CNN (convolutional neural networks) and BILSTM (bi-directional long-short term memory networks) was proposed. Firstly, the multidimensional features were extracted in the time domain, frequency domain, and time-frequency domain, and the sensitive features were screened using monotonicity and trend. Secondly, the sensitive features were feature fused using ASFF-AAKR to construct the health indicators. Finally, the health indicators were inputted into CNN and BILSTM to realize the life prediction of rolling bearings. The results show that the constructed life prediction model is better than other models, and the method has lower error and higher life prediction accuracy.
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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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