Mine airflow speed sensor data cleaning model for intelligent ventilation

doi:10.16265/j.cnki.issn1003-3033.2023.09.1032

Abstract

Abstract:

At present, there are some problems such as incomplete data and information loss in monitoring data cleaning of mine airflow speed sensor. Therefore, a data cleaning method for mine airflow speed sensor based on SDAE was proposed. The airflow speed data samples of the ventilation system under normal conditions were trained by the SDAE algorithm to obtain the upper limit of reconstruction error and tolerance time by kernel density estimation (KDE). By comparing reconstruction error and error duration of test samples with the upper limit of reconstruction error and tolerance time, the "dirty" data was resolved. Using the monitoring data of wind speed sensor in Dongshan coal mine, the data cleaning test of faulty samples and non-faulty samples was carried out. The results show that this method can automatically identify the noise points and missing values. The "dirty" data is repaired directly by reconstruction. In the case of ventilation system fault, this method can effectively retain the state information while filtering out the interference data. Compared with other data cleaning models such as denoising autoencoder (DAE), long short-term memory (LSTM) neural network, and Kalman filter (KF), the average mean absolute error (MAE) and root mean squared error (RMSE) values of this model are reduced by 75.42% and 74.98% respectively.

Key words: mine ventilation, speed sensor, data cleaning, data reconstruction, stacked denoising autoencoder(SDAE)

ZHAO Dan, SHEN Zhiyuan, SONG Zihao, XIE Li'na, LIU Baichen. Mine airflow speed sensor data cleaning model for intelligent ventilation[J]. China Safety Science Journal, 2023, 33(9): 56-62.

Figures/Tables 9

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