ArticleSensors (Basel, Switzerland)2019
Identification and Statistical Analysis of Impulse-Like Patterns of Carbon Monoxide Variation in Deep Underground Mines Associated with the Blasting Procedure.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Research and Simulation on the Development of a Hydraulic Prop Support System of Powered Roof Support to Increase Work Safety.Methods and protocols · 2024Article
- Article
- Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes.Sensors (Basel, Switzerland) · 2020Article
- Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components-A Sieving Screen Case Study.Sensors (Basel, Switzerland) · 2020Article
- Robust Estimation of Carbon Monoxide Measurements.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, to allow miners to start their work, it is crucial to determine the quality of the air. One of the most critical parameters of the air quality is the carbon monoxide (CO) concentration. Thus, in this paper, we analyze the time series describing CO concentration. Firstly, the signal segmentation is proposed, then segmented data (daily patterns) is visualized and statistically analyzed. The method for blasting moment localization, with no prior knowledge, has been presented. It has been found that daily patterns differ and CO concentration values reach a safe level at a different time after blasting. The waiting time to achieve the safe level after blasting moment (with a certain probability) has been calculated based on the historical data. The knowledge about the nature of the CO variability and sources of a high CO concentration can be helpful in creating forecasting models, as well as while planning mining activities.
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Registered trials
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