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Big data analysis platform for mines with time-series database, ID3/C4.5/CART algorithms, fault classification, trend analysis, and anomaly detection for predictive maintenance.
The Mine Big Data Analysis Platform integrates and collects production process, industrial control, management, and equipment fault data from all automation subsystems. By developing big data analysis models for fault classification mining, fault correlation analysis, signal sequence trend analysis, and anomaly detection, the platform enables online condition assessment and diagnosis of automation subsystems — providing technical support for safety management and production operations.
As mining operations become increasingly automated, the volume of data generated by industrial control systems, sensors, and equipment grows exponentially. However, raw data without analysis is merely stored cost. The Mine Big Data Analysis Platform transforms this data avalanche into actionable intelligence by applying sophisticated analytical models that detect patterns, predict failures, and optimize performance across all automation subsystems.
The platform's data acquisition layer automatically interfaces with the comprehensive automation platform, efficiently collecting data from every monitored point. This data is stored in a time-series database architecture that supports automatic time- and space-based partitioning with write-optimized storage — essential for handling the high-velocity, high-volume data streams typical of industrial environments. Statistical analysis functions include maximum, minimum, and average value computation, plus distribution-type evaluation to characterize data behavior patterns.
The platform's analytical intelligence is built on three categories of data mining algorithms: ID3, C4.5, and CART decision-tree mining algorithms. These are applied through four primary analysis models: fault classification mining (categorizing fault types from historical data), fault correlation analysis (identifying which faults tend to co-occur), signal sequence trend analysis (detecting long-term trends, cyclical changes, seasonal patterns, and random variations), and anomaly detection (identifying sequence anomalies, point anomalies, and pattern anomalies). Performance monitoring and prediction functions compute comprehensive equipment performance indices, enabling predictive maintenance strategies.
The platform's alarm modes include performance degradation alarms, parameter over-limit alarms, and fault anomaly early warnings — ensuring that maintenance teams are alerted to developing issues before they escalate into failures or safety incidents. The visualization layer presents monitoring data, KPI indicators, and alarm statistics on a comprehensive large-screen display, giving managers an at-a-glance understanding of the entire mine's automation system health. This transforms maintenance from reactive to predictive, reducing unplanned downtime and extending equipment life.
| Parameter | Value |
|---|---|
| Database Type | Time-series database |
| Mining Algorithms | ID3, C4.5, CART |
| Analysis Models | Fault classification, fault correlation, signal sequence trend, anomaly detection |
| Visualization | Comprehensive large-screen display |
| Alarm Modes | Performance degradation, parameter over-limit, fault anomaly early warning |
Mine automation subsystem condition assessment and diagnosis, electromechanical equipment safety management, safety production data analysis, and predictive maintenance.
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The company is a provider of complete solutions for mine rail transportation dispatching systems and intelligent mine signal control and safety dispatching.
