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The newenergy vehicle industry is faced with comprehensive upgrading and rapid competition, but the product development cycle is continuously shortened, and synchronization brings more severe test on efficiency and cost of R&D equipment. In order to further improve the equipment R&D efficiency and quickly meet the new energy high-end product development needs. A kind of predictive maintenance system for R&D equipment is researched and designed in depth. Fault diagnosis and life prediction algorithm model are developed through key technologies such as Internet of Things, wavelet transform, deep learning, multiple Gaussian distribution and long and short time memory neural network, so as to realize the prediction of key faults and remaining life of equipment. The results show that the system can significantly reduce the downtime and maintenance time, and achieve more efficient use of R&D and maintenance resources.
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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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