基于随机振动的列车和轨道监测MAIANDROS系统

G. Vlachospyros, Ilias Iliopoulos, K. Kritikakos, N. Kaliorakis, S. Fassois, J. Sakellariou, A. Deloukas, G. Leoutsakos, Christos Giannakis, Elias Chronopoulos, Elias Tountas, Dimosthenis Kapiris
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引用次数: 2

摘要

概述了基于随机振动的创新性、车载多用途、铁路车辆和基础设施的MAIANDROS状态监测系统。该系统包括悬架监测(SM)模块、车轮监测(WM)模块、轨道监测(TM)模块(用于轨道分段状态表征)模块、横向稳定性监测(LSM)模块以及车轮等关键部件的剩余使用寿命评估(RULE)模块。它基于统计时间序列类型的方法和适当的决策,旨在克服当前系统的各种挑战,同时推动其性能极限。其独特的优点包括高诊断性能、检测早期(早期)故障的能力、对不同操作条件的鲁棒性、早期检测狩猎的开始、使用最少数量的低成本传感器进行操作,以及实现实时或几乎实时操作的最小计算复杂性。通过雅典地铁车辆上的原型系统的指示性评估和基于SIMPACK的高保真车辆模型的蒙特卡罗模拟,证明了其高可实现性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The MAIANDROS System for Random-Vibration-Based On-Board Railway Vehicle and Track Monitoring
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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