AI-Based Auxiliary Transportation System for Coal Mining

To solve low efficiency and high safety risks in coal mine auxiliary transportation, an AI‑enabled operation system is designed and tested. It integrates real‑time monitoring, intelligent analysis, automatic control, and human‑machine interaction. This blog shows key structure, parameters, and test data with full figures.

1. System Architecture (4 Core Modules)

  • Real-time monitoring module
  • Intelligent analysis module
  • Automatic control module
  • Intelligent control interface module

2. Real-Time Monitoring Module

  • Sensors: PCB352A76 vibration, PT100 temperature, KFG strain sensor
  • Sampling frequency: 1 kHz, accuracy: 0.1 level
  • Communication: CAN bus, EtherCAT, speed up to 100 MB/s
  • Personnel positioning: ZigBee RSSI, accuracy 1 m
  • Dust detection: limit 0.01 mg/m³
  • Databases: InfluxDB, Neo4j

3. Intelligent Analysis Module

  • Hardware: 2×IBM AC922, 1 TB RAM per server; 5×IBM FS9000, total 1 PB storage
  • Software: TensorFlow 2.4, PyTorch 1.8, scikit-learn 0.24
  • AI algorithms: CNN (feature extraction), GRU (time-series anomaly detection)
  • Model: ResNet-50 for high-precision condition assessment
  • Functions: fault classification, RUL prediction, early warning

4. Automatic Control Module

  • Main controller: Siemens S7-1500 CPU 1518-4 PN/DP
  • Drive: Siemens G120 inverter, 0.37–250 kW
  • Communication: PROFINET
  • Control: dynamic model + predictive control + PID self-tuning
  • Equipment: scraper conveyor, belt conveyor

5. Intelligent HMI Module

  • Architecture: B/S mode
  • Server: Dell R740, 2×Intel Xeon Gold 6240
  • Frontend: Vue.js 2.0, ECharts 4.0
  • Mobile APP: real-time alarm push

6. Performance Test Results (120 Days)

Scraper Conveyor SGB620/40T

  • Chain fault diagnosis accuracy: 100%
  • Bearing fault response time: 165 ms
  • Motor fault: high accuracy & fast response

Belt Conveyor DSJ140/140T

  • Tear fault accuracy: 97.25%
  • Response time: 265 ms
  • Deviation & slip faults: fast diagnosis & protection

7. Key Conclusions

  • The AI system realizes real-time monitoring and precise fault diagnosis
  • Response time at millisecond level, accuracy up to 100%
  • Greatly improves safety, stability, and efficiency of auxiliary transportation
  • Provides a solid solution for intelligent coal mine construction

Summary

The AI‑based auxiliary transportation machinery system integrates sensing, AI analysis, automatic control, and visualization. It achieves high‑precision, high‑speed fault diagnosis and automatic protection, significantly enhancing safety and efficiency for coal mining.