Paper Info
Title
Early Warning Of Gas Concentration In Coal Mines Production Based On Probability Density Machine
Abstract
Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.
Year
DOI
Venue
2021
10.3390/s21175730
SENSORS
Keywords
DocType
Volume
gas concentration, coal mines, early warning, class imbalance learning, probability density estimation
Journal
21
Issue
ISSN
Citations 
17
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Yadong Cai100.34
Shiqi Wu200.34
Ming Zhou336.53
Shang Gao491.75
Hualong Yu532522.71