Employing LEWIS1 Accelerometers on the Rail Runner for Strain Estimation

Abstract

Structural Health Monitoring (SHM) plays a vital role in ensuring the safety of railroad infrastructure by enabling the detection of damage, fatigue, and potential failure. While strain is a direct indicator of structural stress and deformation, measuring it reliably across large-scale railroad systems presents significant challenges due to environmental exposure, logistical difficulties in sensor placement, and the limitations of traditional strain gauges. This paper presents an alternative SHM methodology that leverages acceleration data collected from Lightweight Efficient Wireless Intelligent Sensor (LEWIS1) accelerometers, which are low-cost, rapidly deployable, and highly adaptable sensors. The LEWIS1 sensors were deployed aboard the Rail runner train in New Mexico to capture acceleration data during various events such as deceleration, acceleration, steady-state movement, and bridge crossings. By analyzing both time-domain and frequency-domain characteristics, researchers were able to identify distinct time-domain event trends and modal resonances, which form the foundation for future strain estimation using modal decomposition and expansion techniques. This approach addresses the scalability, cost, and robustness issues faced by traditional strain measurement methods and offers a pathway to strain estimation using acceleration-based SHM. Future work will involve integrating these acceleration measurements with a finite element model of the train to estimate global strain distributions throughout train structures and bridge components.