KEYWORDS: Principal component analysis, Education and training, Independent component analysis, Accelerometers, Damage detection, Discontinuities, Design and modelling, Structural health monitoring, Signal processing, Signal detection
Presently, railroad monitoring strategies focus on preventative maintenance by detecting wheel anomalies using wayside detection methods (e.g., wheel-impact load detection), and direct detection of track anomalies using onboard systems (e.g., track geometry vehicles). Both approaches are periodic, manual, and do not support real-time track damage detection. Recent research has focused on detecting damage from acceleration signals obtained onboard moving vehicles and identifying anomalies from derived structural dynamic properties. Though promising due to inherent scalability and cost efficiency, its main goal is to detect damage on the supporting infrastructure and has never before been tested for detecting rail crack damage. Among other reasons, a robust anomaly detection algorithm is missing to allow the industry to embrace an automated and more cost-effective monitoring technique. In this work, we leverage a lab-scale track and moving vehicle actuation system that is scaled with the assistance of industry experts, and comprises a vehicle instrumented with two onboard vertical accelerometers. Cracked rails are simulated by introducing discontinuities (longitudinally and transversely). Several types of feature extraction and dimensionality reduction techniques are employed to evaluate their ability to separate damaged and undamaged records. Inspired from previous work, this work tests the ability of existing data-driven damage detection algorithms to detect local damage by using a novel super modular, precise, and realistically scaled down version of a train-track system. The results of the damage sensitivity show that principal component analysis has the highest balanced combination of recall and true negative rate, compared to other techniques.
KEYWORDS: Design and modelling, LabVIEW, Education and training, Data acquisition, Sensors, Control systems, Computing systems, Aluminum, 3D modeling, Open source software
There is an urgent need to better understand vehicle-rail interaction dynamics to pave the way for more consistent and automated rail crack detection methodologies, as opposed to relying on periodic and manual detection via track circuits or dedicated track geometry cars. Designing an open-source hardware framework for a lab-scale rail testbed would open the doors to further data collection and analysis needed to understand the dynamic response of cracked rails. We present a framework and the corresponding open-source hardware and software (published to GitHub) for developing a laboratory-scale motorized railroad testbed, with a vehicle that is modularly tuned to the dynamics of an in-service rail car.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.