Abstract
As crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitigate such risks. In this work, a new parameter-related Wiener process model is proposed to capture multiple uncertainties existed in on-site prognosis of wheel treads. The proposed model establishes a quantitative relationship between degradation rate and variations, and integrates uncertainties via heterogeneity analysis of both criterions. A maximum-likelihood-based method is presented to initialize the unknown model parameters, followed by a recursive update algorithm with fully utilization of historical lifetime information. An investigation of real-world wheel tread signals demonstrates the superiority of the proposed model in accuracy improvement.
Highlights
As a fast and convenient transportation asset, high-speed trains have drawn world-wide attention in recent years
We found that the second wheel tread performed a two-phase and rapid degradation phase.itsResults are presented
We found that the second wheel tread that this was reasonable as the health states of the wheel tread could be different from each other before performed a two-phase behavior withinthe itschanging rapid degradation phase
Summary
As a fast and convenient transportation asset, high-speed trains have drawn world-wide attention in recent years. Under high-speed operating conditions, health monitoring for rotary machinery systems is a crucial technology to guarantee the safety and reliability of high speed trains [1]. With the rapid development of sensor technologies, multiple sources of operational/health data can be collected, supporting online diagnosis and prognosis [2,3,4]. For high-speed trains, engineers collect vibration signals of rotary machinery systems such as bearings, gears, axles and wheel treads via on-board monitoring device and conduct fault recognition, location and isolation based on signal processing technologies. Diagnosis only provides failure information and provides no guidance for preventive maintenance, which is quite crucial to mitigate malfunction risks. It is of urgent necessary to develop advanced prognostic methodologies, which predicts real-time health information to support condition-based maintenance plan
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