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AI-Powered Real-time Monitoring System for Mining Support

softwaremind.com

Energy & natural resources

Challenges with Mining Support Monitoring

CoalMine Polska currently lacks a robust, real-time monitoring system for mining support. This deficiency results in delayed detection of critical failures, such as potential mine shaft collapses and mining shearer malfunctions. These failures pose significant safety risks to miners and cause costly interruptions in coal extraction.

About the Client

A leading Polish company involved in coal mining operations, focused on enhancing safety and operational efficiency.

Project Goals

Develop a real-time monitoring system for mining support to proactively identify potential failures.
Enhance miner safety by providing early warnings of critical risks.
Minimize operational disruptions caused by equipment failures.
Improve the efficiency of coal extraction through reduced downtime.

System Functionality

Real-time data ingestion from mining equipment and sensors.
AI-powered anomaly detection for predictive failure analysis.
Automated alerts and notifications for critical events.
Historical data analysis and reporting.
User-friendly dashboard for monitoring system status and equipment health.

Preferred Technologies

Apache Spark

Azure Data Lakehouse

Azure ML

Azure Synapse Analytics

Power BI

Required Integrations

Existing mining equipment data systems
Sensor data streams
Communication systems within the mine

Non-Functional Requirements

High scalability to handle large volumes of data.
Low latency for real-time monitoring and alerting.
Robust security measures to protect sensitive data.
High availability and reliability.
Data privacy compliance

Expected Business Impact

Implementation of this AI-powered monitoring system is expected to significantly improve miner safety, reduce equipment downtime, optimize coal extraction processes, and minimize operational costs. The proactive failure prediction capabilities will enable timely interventions, preventing costly accidents and disruptions. Improved data-driven decision making will contribute to overall operational efficiency and profitability.