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In view of the frequent failure of power generation equipment under the background of frequent deep peak regulation, flexible operation, energy saving and consumption reduction of thermal power units, a coal mill fault warning method based on multiple state estimation-analytic hierarchy process is proposed. Firstly, based on the characteristic parameters of coal mill Spearman correlation analysis for dimension reduction, equidistant sampling method is used to extract some samples from a large number of coal mill history data memory matrix, after normalization, memory matrix is formed. Then, multi-state estimation algorithm is adopted to calculate the corresponding memory estimated vector according to the memory matrix and the observation vector. The characteristic parameters are given different weights by using analytic hierarchy process (AHP), and the fusion similarity between the observed vector and the estimated vector is calculated, and the fault warning of coal mill is carried out based on the adaptive threshold method. Finally, the actual fault data of a roller medium speed coal mill is taken as an example to verify the effectiveness of the method. The results show that, this method has less misalarm rate and false alarm rate for coal mill fault warning, which can reduce the actual fault probability of coal mill to a certain extent.
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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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