Communication of Sensor Data in Underground Mining Environments: An Evaluation of Wireless Signal Quality over Distance

Ikeda, Hajime; Kolade, Oluwafemi; Mahboob, Muhammad Ahsan; Cawood, Frederick; Kawamura, Y.

doi:10.3390/mining1020014

Cited by 18 publications

(9 citation statements)

References 35 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The formation of the problem was undertaken subsequent to the finalization of the predetermined set of criteria and securing sensors. As revealed in Figure 2 , Accuracy (C1) [ 15 ], Reliability (C2) [ 16 ], Durability (C3) [ 17 ], Cost (C4) [ 11 ], Ease of Installation (C5) [ 18 ], Power Consumption (C6) [ 19 ], Response Time (C7) [ 20 ], Interoperability (C8) [ 21 ], Maintenance Requirements (C9) [ 22 ], Data Integration (C10) [ 23 ], Environmental Impact (C11) [ 24 ], Scalability (C12) [ 25 ], Regulatory Compliance (C13) [ 26 ], Adaptability to Varied Conditions (C14) [ 27 ], and User-Friendliness (C15) [ 28 ] are determined the most important criteria. Figure 2 illustrates the entirety of the decision system components examined in this current study.…”

Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

View full text Add to dashboard Cite

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

show abstract

Section: Methodsmentioning

confidence: 99%

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Wang,

Cheng,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The important features the smart watch has for monitoring of the mine worker's state of hearing are: Mine worker's real-time location tracking, incident reporting and feedback-based communication. The mine workers can use the smart watch to report on incidences related The data transmission diagram (Ikeda et al, 2021). to excessive noise that could be coming from faulty equipment or malfunctioning hearing protection.…”

Section: Demographics and Inclusion Criteriamentioning

confidence: 99%

“…At present, various wired ( Dohare et al, 2015 ; Ikeda et al, 2021 ; Kolade et al, 2021 ; Kolade and Cheng, 2021 ) and wireless communication technologies are available that meet the minimum mandatory criteria for the data broadcast speed and range to support remote mining operations and advanced monitoring systems. The data transmission diagram by Ikeda et al (2021) and Figure 2 .…”

Section: Introductionmentioning

confidence: 99%

“…At present, various wired(Dohare et al, 2015;Ikeda et al, 2021; and wireless communication technologies are available that meet the minimum mandatory criteria for the data broadcast speed and range to support remote mining operations and advanced monitoring systems. The data transmission diagram byIkeda et al (2021) and Figure2.The internet and WIFI technologies that are currently implemented in the mines ensure the efficient transmission and transfer of information. The transmission diagram in Figure2demonstrates how the smart technology is integrated with hearing protection.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Madahana,

Ekoru,

Sebothoma

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionOccupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers.MethodologyThe system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the “surface” of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4–2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years.Results and discussionPreliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.

show abstract

“…It is in this context that the monitoring of sustainability and safety in mining has become a relevant value in the productive chain of the extractive activity of metals, being one of the fundamental pillars of corporate social responsibility (CSR) [ 33 ]. To assess and measure the performance of mining companies in sustainability and safety aspects, a series of indicators are being implemented, such as the Environmental, Social, and Governance (ESG) indicators for sustainability and Key Performance Indicators (KPIs) for safety [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Cacciuttolo,

Guzmán,

Catriñir

et al. 2023

Sensors

View full text Add to dashboard Cite

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.

show abstract

Communication of Sensor Data in Underground Mining Environments: An Evaluation of Wireless Signal Quality over Distance

Cited by 18 publications

References 35 publications

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining

Contact Info

Product

Resources

About