Machine Vision Condition Monitoring of Heavy-Axle Load Railcar Structural Underframe Components

Abstract

To ensure the safe and efficient operation of the approximately 1.6 million freight cars (wagons) in the North American railroad network, the United States Department of Transportation (USDOT), Federal Railroad Administration (FRA) requires periodic inspection of railcars to detect structural damage and defects. Railcar structural underframe components, including the centre sill, sidesills, and crossbearers, are subject to fatigue cracking due to periodic and/or cyclic loading during service and other forms of damage. The current railcar inspection process is time-consuming and relies heavily on the acuity, knowledge, skill, and endurance of qualified inspection personnel to detect these defects. Consequently, technologies are under development to automate critical inspection tasks to improve their efficiency and effectiveness. Research was conducted to determine the feasibility of inspecting railcar underframe components using machine vision technology. A digital video system was developed to record images of railcar underframes and computer software was developed to identify components and assess their condition. Tests of the image recording system were conducted at several railroad maintenance facilities. The images collected there were used to develop several types of machine vision algorithms to analyse images of railcar underframes and assess the condition of certain structural components. The results suggest that machine vision technology, in conjunction with other automated systems and preventive maintenance strategies, has the potential to provide comprehensive and objective information pertaining to railcar underframe component condition, thereby improving utilization of inspection and repair resources and increasing safety and network efficiency.