Description:
Reference #: 1701
The University of South Carolina is offering licensing opportunities for Rail surface defect detection from onboard vibration sensors.
Background:
Maintaining the railroad infrastructure in a state of good repair to prevent failure and service disruptions is a challenge of paramount importance to the safety and economy of operations. Rail Surface Spot Irregularities (RSSI) are among the most common types of damage in railway networks that affect track operating conditions and dramatically increase the risk of rail break, track failure and derailments. Such irregularities are local defects on the rail surface arising from rolling contact fatigue (e.g. squats, spalling, or shelling), joints and welds, among others, and will grow as load accumulates. RSSI sizes (wavelengths) vary from about a few centimeters when they initially form to as much as one meter after load accumulation. Relative to random irregularities, RSSI consists of shorter wavelengths that induce significant dynamic excitation on the track and track structures. RSSI significantly increases the wheel-rail dynamic forces and the wheel unloading rate. Despite the localized nature of RSSI damage, both the track and train responses are impacted. As a result of such high stress fluctuations, components of the rail system deteriorate rapidly leading to worsening of operating conditions and safety and lack of early detection and mitigation may lead to catastrophic events.
Invention Description:
This invention is a new algorithm developed to detect early signs of rail surface defects, known as Rail Surface Spot Irregularities (RSSI), on train tracks. The proposed technology is an automated system for RSSI detection, localization and severity evaluation that combines acceleration sensors, edge computing, and digital communications in one, low-cost, fast, accurate, and integrated system that allows for continuous rail surface health monitoring at train operating speeds. At the core of the proposed system is a novel, and efficient damage identification algorithm developed by the research team for rapid RSSI detection and characterization. By leveraging real data from in-service trains and employing the proposed novel combination of advanced signal processing techniques, this technology can identify minor, early stage RSSI, enabling prompt and cost-effective maintenance.
Potential Applications:
Railroad maintenance industry
Advantages and Benefits:
Traditional practices for RSSI detection involve manual inspection by trained personnel, which is labor-intensive, inefficient, and subject to inspector’s skill level. Current development efforts focus on automated detection systems, including ultrasonic, eddy current, magnetic flux leakage, laser scanning, and image processing techniques. However, these techniques face practical constraints such as train speed requirements, limited deployment, high equipment costs, and reliability issues. Recent advancements in AI and machine learning-based optical inspection techniques show promise but are sensitive to factors like lighting conditions and vehicle speed, which affect image quality and detection accuracy.
This approach not only reduces time and resource expenditures but also enables real-time defect identification, facilitating prompt corrective action.