AI-BASED PREDICTIVE MAINTENANCE FOR AUTOMATED CONVEYOR SYSTEMS IN UNDERGROUND MINING

Azizov Ozodbek Farxod o'g'li

Authors

Azizov Ozodbek Farxod o'g'li Nukus State Technical University Student of Mining Electrical Engineering

Keywords:

underground mining, automated conveyor system, predictive maintenance, artificial intelligence, real-time monitoring, sensor systems, operational efficiency, fault detection.

Abstract

In modern underground mining, the reliable operation and continuous performance of conveyor systems are crucial for ensuring production efficiency. Traditional maintenance methods are often reactive, addressing failures only after they occur. Such approaches reduce operational efficiency and incur additional costs. In recent years, artificial intelligence (AI) technologies have enabled predictive maintenance capabilities for conveyor systems. AI-based systems analyze real-time data collected from sensors, evaluate the operational condition of equipment, and help plan maintenance by predicting potential failures in advance. This article examines the application of AI technologies to enhance the efficiency of automated conveyor systems in underground mines, reduce downtime, and minimize operational costs. The study reviews advanced practices in both Uzbekistan and international mining industries, sensor systems, data collection and analysis algorithms, and predictive maintenance approaches. Furthermore, the article discusses the potential of AI to improve operational safety, prevent failures, and stabilize production processes.

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