This application provides a railway bridge health
monitoring system and method based on
infrared thermal imaging. The
data acquisition device uploads the collected
monitoring data to the monitoring center via a local monitoring module. The monitoring center sets a
disease alarm threshold; if the difference between the average temperature of the monitored area and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential
disease in the monitored area. Then, sensor data from the monitored area is acquired to determine the type of
disease, and the disease level is determined based on the bridge temperature data in the monitored area. An alarm is issued and a handling plan is displayed. Simultaneously, the monitoring center performs
machine learning on the
monitoring data to predict potential disease risks, issue early warnings, and display handling strategies. The technical solution provided in this application comprehensively analyzes the bridge temperature data acquired by the
infrared thermal imaging camera and the sensor data acquired by the sensors to determine the type and level of disease and display handling plans, thereby improving the efficiency of detection and maintenance.