Rolling-Stock Automatic In-Situ Line Deterioration and Operating Condition Sensing

Abstract

Track and equipment failures dominate railroad accident causes. Railroads must visually inspect most tracks in service as often as twice weekly to comply with the Federal Track Safety Standards. They augment visual inspections with automated non-destructive-evaluation (NDE) equipment to locate developing and mature defects. However, the defect formation rate is escalating with increasing traffic load density and continuously declining railroad employment per track-mile. This indicates a widening gap between the rate of defect formation and the resources available to find them before they result in accidents, delays, and lost revenue. With resources thinly stretched and the rate of defect formation escalating with traffic load-density, railroads are seeking to enhance the efficiency of inspections and maintenance of way. This paper describes the development of a Rolling-stock Automatic In-situ Line Deterioration & Operating Condition Sensing (RAILDOCS) system to automatically locate and classify track and rail vehicle defects. The approach incorporates a new low-cost wireless sensor technology and Cloud computing method to guide and focus inspection activities to locations of equipment and track defect symptoms, leading to efficient diagnosis and remediation. RAILDOCS has on-board sensors which will continuously monitor track and vehicle condition and transmit a 3D inertial signature for a remote processor to analyze and produce a complete and updated picture of aggregate track and equipment quality. RAILDOCS complement more expensive visual and NDE methods by reallocating time spent on defect discovery to detailed inspections of prioritized defect symptom locations. Symptom sensors integrate micro-electro-mechanical (MEMS), global positioning system (GPS) satellite receivers, wireless communications, and microprocessors technology. Cloud computing and signal processing algorithms produce a track quality index, and forecast optimum maintenance triggers.