Design And Implementation Of Image Analysis System For Locomotive Maintenance

With the in-depth advancement of railway informatization construction and the rapid development of modernization and intelligentization of locomotive equipment,especially the widespread application of technology including Internet of Things,mobile terminal and image processing,more higher requirements have been imposed on railway locomotive management.The railway maintenance department,as the driver of the railway transportation,shoulders the sacred mission of providing traction power to ensure the s mooth transportation of railways and guarding the last line of defense of railway transportation safety.Locomotive maintenance is the basis for ensuring high locomotive quality and adequate traction power supply.This thesis introduces the application of Internet of Things,image recognition,mobile terminal technologies in the management of locomotive maintenance,which effectively strengthened the monitoring of employees' behavior and equipment status during the operation process,further enriched the me thods for the safety of locomotive operation and equipment quality stable and reliable,Continually improved capabilities of the physical and technical defense for locomotive maintenance equipment.According to the development requirements of intelligent maintenance and specialized management of locomotive maintenance,the thesis deeply analyzes the existing problems in the current locomotive maintenance and servicing operation,and conducts in-depth learning on image recognition,Internet of Things technology,TensorFlow platform and mobile terminal applications,and embed image recognition into the locomotive maintenance operations with advanced communication technologies such as WLAN,5G and WiFi.Adopting advanced information integration technology and software programming methods,the existing information resources of locomotive maintenance have been integrated.Focusing on locomotive quality management,operation safety management,work progress management,operation data collection,operation process a nalysis,the information processing flow of image Analysis,system goals,functional requirements and performance requirements of locomotive maintenance are analyzed;The system functional architecture,overall framework were designed in detail.A deep learning framework based on image recognition and image recognition training methods were constructed to continuously strengthen process management and effectively eliminate the weaknesses in the existing maintenance and operation mode.Finally,the system provides effective information support for locomotive maintenance quality management,improvement of operation methods,and optimization of operational procedures through statistical analysis of massive image data of locomotive maintenance.