Tunnel Machine Monitoring via Big Data Analysis
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Solution Overview
Problem
Current monitoring and analysis methods for large tunnel machines lack detailed and accurate analysis of operation trajectories and internal health, leading to inefficient tunnel excavation, increased energy consumption, and reduced service life due to fluctuating rotating speeds and inadequate manual acceptance processes.
Innovation Solution
A method for monitoring and analyzing large tunnel machines using automatic big data collection, which involves dividing tunnel operation areas, obtaining environmental information, calculating rock drilling difficulty coefficients, confirming operation trajectories, analyzing conformity, processing abnormalities, and evaluating the health state of tunnel rock drills, incorporating soil moisture, geological data, and laser scanning for precise monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual acceptance method is used to detect completion of tunnel excavation, then the process can be performed with simple equipment, but the acceptance steps become complicated and consume a lot of manpower
Solution Approach 1:
The patent replaces manual acceptance methods with an automated monitoring system that uses sensors to detect tunnel excavation completion. The system automatically collects data from multiple sensors (position, temperature, humidity, gas concentration) and processes it through a control unit to determine whether excavation is complete, eliminating the need for complex manual acceptance procedures and reducing manpower requirements.
2Ease of operation
If rotating speed of the steel is controlled only by the experience of the staff, then the operation can be performed with simple control, but the rotating speed fluctuates and does not meet the actual geological conditions
Solution Approach 1:
The patent implements a feedback control system where sensors continuously monitor geological conditions (rock hardness, temperature, humidity) and the control unit adjusts the rotating speed of the steel based on this real-time data. The system compares actual conditions with optimal parameters and automatically adjusts the rotating speed to maintain optimal performance, ensuring precise control that adapts to varying geological conditions throughout the tunnel excavation process.
3Device complexity
If internal parts of the machine are not monitored, then the monitoring system remains simple, but the internal damage of the tunnel rock drill cannot be understood timely
Solution Approach 1:
The patent divides the monitoring system into multiple independent sensor modules that monitor different aspects of the tunnel rock drill's internal parts. Each sensor (temperature, vibration, pressure) focuses on specific components, and the control unit integrates data from all segments to provide comprehensive internal damage detection. This segmented approach allows thorough monitoring without requiring a single overly complex monitoring system.
4Productivity
If detailed and accurate analysis of operation trajectory is not performed, then the analysis process remains simple, but the operation efficiency and service life are reduced
Solution Approach 1:
The patent replaces simple operation monitoring with an automated trajectory analysis system that uses position sensors, GPS, and data processing algorithms to continuously track and analyze the operation trajectory of the tunnel rock drill. The control unit processes this data to provide detailed insights into drilling paths, efficiency metrics, and operational patterns, enabling precise optimization of excavation operations and extending equipment service life through data-driven decision making.
Data Source
AI summary
A method for monitoring and analyzing large tunnel machines based on automatic collection of big data includes the following steps: dividing a tunnel operation area, obtaining environmental information of a tunnel operation sub-area, analyzing rock drilling difficulty of the tunnel operation sub-area, confirming a tunnel operation trajectory, analyzing conformity of the tunnel operation sub-area, processing an abnormal tunnel operation sub-area, and analyzing health state of a tunnel rock drill. According to the present disclosure, a rock drilling difficulty coefficient of each tunnel operation sub-area is used to analyze a corresponding steel rotating speed, and then the tunnel operation trajectory is confirmed. After the tunnel operation is completed, the operation conformity of each tunnel operation sub-area is analyzed, and each abnormal tunnel operation sub-area is screened out and processed accordingly.