Full
Outline
A turbocharger surge identification method based on multi-domain composite features of non-intrusive acoustic signals was proposed on a turbocharged diesel engine. Firstly, the data of surge acoustic signals were collected and relevant database was established through the turbocharger surge reproduction experiment. Then, multiple features were selected and extracted from the time, frequency and cepstrum domains. The contribution of each feature to the wheezing was calculated using the random forest algorithm, and the core contributing features were selected and combined into a multi-domain composite feature, which was used as the sensitive feature of the wheezing acoustic signals for the identification of the turbocharger wheezing. Furthermore, the support vector machine was used to establish a model for the identification of the wheezing, and the whale optimization algorithm was applied to optimize the coefficients of the kernel function of the support vector machine with the objective of the identification accuracy. Finally, multiple acoustic signals were obtained through the turbocharger bench test, and the multi-domain composite features of the acoustic signals were solved to verify the accuracy of the model. The results show that the accuracy of the model can reach more than 99%.
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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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