The feasibility of on-board remaining useful life (RUL) estimation of hollow worn railway vehicle wheels under varying travelling speed using vibration signals from the vehicle’s bogie, is for the first time explored. A variant of the Multiple Model Power Spectral Density (MM-PSD) method is employed, treating RUL estimation as a multi-class classification problem, where each class corresponds to a specific condition of hollow worn wheels associated with an empirical remaining mileage. The method classifies the current, unknown, condition of hollow worn wheels to one of the available by examining the vehicular dynamics similarity among them through the amplitude of the sharp periodic valleys (antiresonances) induced due to the Wheelbase Filtering (WF) effect. The vibration signals are obtained from Monte Carlo experiments based on the high-fidelity SIMPACK software, while the remarkable performance of the method is demonstrated via thousands of inspection cases.
A bird’s–eye overview of the innovative, on–board and Multi–Purpose, random vibration based MAIANDROS Condition Monitoring system for railway vehicles and infrastructure is presented. The system includes Modules for Suspension Monitoring (SM), Wheel Monitoring (WM), Track Monitoring (TM) for track segment condition characterization, Lateral Stability Monitoring (LSM), and Remaining Useful Life Estimation (RULE) for critical components such as wheels. It is based on Statistical Time Series type methods and proper decision making, and aims at overcoming various challenges of current systems while pushing their performance limits. Its unique advantages include high diagnostic performance, ability to detect early–stage (incipient) faults, robustness to varying Operating Conditions, early detection of the onset of hunting, operation with a minimal number of low–cost sensors, and minimal computational complexity for achieving real–time or almost real–time operation. Its high achievable performance is demonstrated via indicative assessments using a prototype system onboard an Athens Metro vehicle and Monte Carlo simulations with a SIMPACK based high–fidelity vehicle model.
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