Intelligent Preload Monitoring Technology for Fastening Systems and Early Warning Scheme for Line Safety
What are the core technical parameters of the intelligent preload monitoring sensor for fastener systems?The core technical parameters of the intelligent preload monitoring sensor for fastener systems include measurement range, accuracy, operating temperature and wireless transmission distance, which directly determine the monitoring effect. The measurement range should cover the preload interval of the fastener system, usually 0-500N·m, to meet the preload monitoring needs of bolts on different lines. The measurement accuracy must reach ±1%FS to ensure data accuracy and avoid false alarms due to insufficient accuracy. The operating temperature range is -40℃～80℃, adapting to the extreme climate environment of railway lines, and can work stably in alpine and high-temperature areas. The wireless transmission distance is ≥100m, adopting LoRa wireless communication technology with low power consumption and stable transmission, realizing long-distance data transmission between sensors and base stations. In addition, the protection grade of the sensor must reach IP68, waterproof and dustproof, adapting to the humid environment of the ballast bed.
What are the composition architecture and working principle of the intelligent preload monitoring system?
The intelligent preload monitoring system adopts a three-level architecture of "sensor-base station-cloud platform" to realize the collection, transmission, analysis and early warning of preload data. The first level is a micro strain sensor built into the bolt, which collects the strain data of the bolt in real time and converts the strain value into a preload value through a built-in chip. The sampling frequency is set to 1 time per minute, and it can work continuously for more than 5 years in low-power mode, which avoids frequent battery replacement and reduces maintenance costs. The second level is the on-site base station, which is usually installed on the signal poles beside the track. Each base station can connect 200-300 sensors at the same time, receive the data transmitted by the sensors and perform preliminary filtering to eliminate invalid data caused by environmental interference. The third level is the cloud platform, which receives the data uploaded by the base station through the mobile network and stores it in a database. The cloud platform uses a preload attenuation model to analyze the data. When the preload value drops to the set threshold, it will automatically trigger an early warning signal and push it to the maintenance personnel's terminal. The working principle of the system is based on the linear relationship between bolt strain and preload. The sensor monitors the strain change of the bolt caused by load and temperature, calculates the real-time preload value, and realizes dynamic monitoring and early warning of preload attenuation.
What are the differentiated settings of preload monitoring thresholds for different line types?
The differentiated settings of preload monitoring thresholds for different line types are mainly based on the load characteristics and operation safety requirements of the lines. For high-speed railways with operating speeds of 250-350km/h, the stability of the fastener system has a crucial impact on driving safety, so the monitoring threshold is set to 85% of the designed preload. Once the preload drops to this value, an early warning will be triggered immediately to avoid potential safety hazards caused by insufficient preload. For heavy-haul railways with large axle loads, the bolts are subject to large impact loads, and the preload attenuates faster. The monitoring threshold is set to 80% of the designed preload, and an impact load monitoring threshold is added at the same time. When the impact load exceeds 120% of the designed value, a synchronous early warning will be issued to remind maintenance personnel to check the bolt status in time. For ordinary-speed railways with low operating speeds and small loads, the requirements for preload stability are relatively loose, and the monitoring threshold is set to 75% of the designed preload, which balances early warning accuracy and maintenance costs. For urban rail transit lines with frequent starts and stops, the preload of bolts fluctuates greatly. The monitoring threshold is set to 80% of the designed preload, and the fluctuation frequency monitoring is added. When the fluctuation frequency exceeds 10 times per hour, an early warning will be triggered to prevent bolt loosening caused by frequent vibration.
What are the key installation process points of preload intelligent monitoring sensors?
The key installation process points of preload intelligent monitoring sensors are concentrated on sensor implantation and bolt assembly, which directly affect the monitoring accuracy and reliability. The sensor implantation adopts an embedded installation method. First, a hole is drilled in the bolt shank with a diameter 0.1-0.2mm larger than the sensor diameter and a depth of 1/3 of the bolt length. This depth can ensure that the sensor is located in the stress concentration area of the bolt and accurately collects strain data. After drilling, the debris in the hole must be cleaned with a high-pressure air gun, and then the sensor is implanted into the hole and fixed with epoxy resin adhesive to ensure that there is no gap between the sensor and the bolt, avoiding measurement errors caused by relative movement. During bolt assembly, a special torque wrench must be used, and the tightening torque must be strictly implemented according to the design value. The tightening torque of bolts for high-speed railways is 350-400N·m, and that for heavy-haul railways is 400-450N·m. Excessive torque will cause plastic deformation of the bolt, while insufficient torque will affect the normal operation of the sensor. After installation, calibration is required. A standard torque meter is used to apply different torques to verify the measurement accuracy of the sensor, ensuring that the error is ≤±1%FS. In addition, the wireless antenna of the sensor must face the base station to ensure stable signal transmission.
What are the on-site maintenance and early warning disposal processes of the preload intelligent monitoring system?
The on-site maintenance and early warning disposal processes of the preload intelligent monitoring system include three links: sensor maintenance, data calibration and early warning disposal. The sensor maintenance cycle is 1 year. The main work is to check the battery power and signal strength of the sensor. When the power is lower than 20%, the battery should be replaced in time to avoid monitoring interruption. When the signal strength is weak, the direction of the antenna should be adjusted or the position of the base station should be optimized to ensure smooth data transmission. The data calibration cycle is 6 months. A standard torque meter is used to calibrate the sensor to eliminate the measurement deviation caused by sensor aging and ensure that the measurement accuracy meets the requirements. The early warning disposal process is divided into three levels: for level 1 early warning (preload 80%-85%), the system pushes the warning information to the line maintenance terminal to remind maintenance personnel to pay attention to the bolt status; for level 2 early warning (preload 75%-80%), maintenance personnel are arranged to conduct on-site inspections and retighten the bolts to restore the preload to the design value; for level 3 early warning (preload <75%), the line is immediately blocked, the bolts are replaced, and potential safety hazards are eliminated. After the disposal is completed, the disposal results must be recorded in the cloud platform to update the monitoring data and form a closed-loop management of preload monitoring.