Review of Fault Diagnosis and Early Warning of Coal Mine Ventilator
Journal Title: International Journal of Advanced Research in Science and Technology (IJARST) - Year 2024, Vol 13, Issue 4
Abstract
The fault diagnosis and early warning system for coal mine ventilators play a pivotal role in ensuring the safety and efficiency of underground coal mining operations. This review delves into the state-of-the-art techniques, methodologies, and advancements in fault diagnosis and early warning systems specifically tailored for coal mine ventilators. The review encompasses a comprehensive analysis of various approaches, including traditional methods, such as rule-based systems and expert systems, as well as modern techniques like machine learning algorithms, artificial intelligence, and data- driven models. It examines the challenges associated with fault diagnosis in coal mine ventilators, such as the complex and harsh operating environment, limited data availability, and the need for real-time monitoring. Furthermore, the review discusses the importance of early warning systems in mitigating potential hazards and preventing catastrophic incidents in coal mines. It explores the role of sensor technologies, data acquisition systems, and communication protocols in enabling timely detection and notification of abnormalities in ventilator performance.
Authors and Affiliations
V. Srujan kumar1, D. Akhil2, G. Vikas3,CH. Gopi4
Keywords
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- EP ID EP738872
- DOI 10.62226/ijarst20241371
- Views 45
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