Identification of air door opening and closing stage based on DWT and SVM

doi:10.16265/j.cnki.issn1003-3033.2023.01.0846

Abstract

Abstract:

In order to solve the false alarm problem of wind speed sensor and abnormal fluctuation of wind speed sensor data caused by air door opening and closing, a recognition method of air door opening and closing stage based on DWT and SVM was proposed. A multi-scale sliding window was used to discretize the continuous wind speed monitoring data into several sub-time series of different scales. Statistical methods and DWT were used to extract statistical features and hidden wave features in discrete data of sub-time series data of various scales, and SVM air door opening and closing stage recognition and classification models were established. In order to further optimize the recognition results, the classification recognition results were merged, modified, combined and optimized based on the IoU rule. Finally, according to the similarity criterion, the similarity experimental model with the length direction variable rate of 2 and the overall similarity ratio of 1∶16 was established, and the feasibility of the method was verified by the similarity test of the air door opening and closing disturbance. The results show that the recognition accuracy of this method is high in the test set, the recognition accuracy of air door opening and closing time can reach 90.08%, while the classification accuracy of air door opening and closing stage can reach 71.05%. Hence the optimization on the number of sliding window scales can increase the recognition accuracy.

Key words: discrete wavelet transform (DWT), support vector machine (SVM), air door opening and closing, stage identification, multiple-scale sliding window, intersection over union (IoU)

DENG Lijun, SHANG Wentian, LIU Jian, ZHOU Yukai, SONG Ying. Identification of air door opening and closing stage based on DWT and SVM[J]. China Safety Science Journal, 2023, 33(1): 95-104.

Figures/Tables 11

Fig.1

Fig.2

Tab.1

Comparison of data characteristics of 4 and 14 length sub-time series

长度	类别	$x -$	$x m i n$	$x m a x$	$σ 2$	$m 1 s$	$m 1 d$	$m 2 s$	$m 2 d$	$m 3 s$	$m 3 d$
4	正常通风时间	0.373	0.356	0.394	0.016	0.003	1.025	0.006	0.471	—	—
4	风门开闭时间	0.949	0.962	0.942	0.009	0.001	-3.068	0.003	-6.674	—	—
14	正常通风时间	0.207	0.275	0.131	0.047	0.041	2.112	0.067	1.664	0.026	0.051
14	风门开闭时间	0.893	1.000	0.778	0.076	0.067	-7.837	0.137	-21.898	1.004	-37.738

Tab.1

Fig.3

Fig.4

Fig.5

Fig.6

Tab.2

Fig.7

Fig.8

Fig.9

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影响因素	设定参数
风门开闭速度/((°)·s^-1)	3 、5、10、15
风门开闭角度/(°)	45、60、75、90
风门敞开时间/s	5、10、15、20、30 40