The working principle of conveyor belt monitoring systems is primarily based on four core components: sensor perception, data transmission, central control, and intelligent analysis. By integrating automation control with artificial intelligence (AI) technology, the system achieves real-time monitoring, fault early warning, and coordinated control of the conveyor belt's operational status.
System Composition and Data Acquisition
- Sensor Network: Various sensors are deployed to collect real-time operational parameters, including:
- Protection Sensors: Speed, misalignment (deviation), pile-up, tear, smoke, temperature, emergency stop, tension, and vibration sensors.
- Visual Equipment: HD cameras, infrared thermal imagers, and laser profile scanners.
- Specialized Monitoring Devices: Such as weak magnetic flaw detectors (for detecting damage in steel cord cores), radar level meters, and fiber optic temperature/acoustic sensors.
- Data Acquisition and Processing: Sensor signals undergo signal conditioning (amplification, filtering) and analog-to-digital conversion (ADC), followed by preliminary processing by a PLC (Programmable Logic Controller) or edge computing units.
Data Transmission and Communication
Industrial communication protocols (such as PROFIBUS, CAN, RS485, and Ethernet) are used to upload data to the ground dispatch center.
Critical systems (such as belt conveyors in coal mines) utilize fiber optic ring networks to achieve high-speed, reliable communication, typically with a response time of less than 150 milliseconds.
Supports a multi-level network architecture: Field Sub-stations → Communication Interfaces → Ground Master Station (Host Computer System).
Central Monitoring and Intelligent Analysis
Host Computer System: Deployed in the dispatch center and composed of industrial computers and SCADA/configuration software, it displays in real-time:
- Equipment operational status (start/stop, speed, current, etc.).
- Fault alarm information (including specific sensor locations).
- Video feeds (supporting playback, recording, and multi-screen switching).
AI and Intelligent Recognition
Utilizes YOLO algorithms or deep learning models to identify no-load conditions, material pile-ups, large foreign objects, and open flames.
Steel cord weak magnetic monitoring systems use spatial magnetic field vector synthesis to achieve a 99% detection accuracy rate for hidden damages such as broken cords and splice slippage.
Intelligent belt tear detection employs laser and machine vision to identify millimeter-level longitudinal tears, with a response time of ≤ 0.1 seconds.
Control and Protection Mechanisms
Multi-Mode Control Methods
- Remote Control: Unified start/stop from the ground dispatch center, following the logic of starting against the material flow and stopping with the material flow.
- Centralized Control: The master station console coordinates the interlocked operation of multiple devices.
- Local Control: Single-machine operation with interlocking logic (if the preceding device stops, the following one stops).
- Maintenance Control: Independent start/stop with no interlocking.
Automatic Protection Actions
When a severe fault is detected (such as tearing, emergency stop, or overheating), the system automatically shuts down and triggers audio-visual alarms.
- Supports a three-level alarm mechanism: Prompt → Audio-Visual Alarm → Coordinated Shutdown.
- Human-Machine Interaction (HMI): Color LCD screens display operational status, fault localization, and historical records, supporting operator login and permission management.
