Reliability Analysis for Degradation of Locomotive Wheels using Parametric Bayesian Approach

Abstract

This paper undertakes a reliability study using a Bayesian survival analysis framework to explore the impact of a locomotive wheel's installed position on its service lifetime and to predict its reliability characteristics. The Bayesian Exponential Regression Model, Bayesian Weibull Regression Model and Bayesian Log-normal Regression Model are used to analyze the lifetime of locomotive wheels using degradation data and taking into account the position of the wheel. This position is described by three different discrete covariates: the bogie, the axle and the side of the locomotive where the wheel is mounted. The goal is to determine reliability, failure distribution and optimal maintenance strategies for the wheel. The results show that: (i) under specified assumptions and a given topography, the position of the locomotive wheel could influence its reliability and lifetime; (ii) the Bayesian Log-normal Regression Model is a useful tool.

IN2CLOUD: A novel concept for collaborative management of big railway data

“…approaches would be useful for updating model parameters, models, knowledge, and benchmarking to further achieve global optimizations (a simple example will be discussed later). However, compared with studies that would effectively achieve local optimizations (Cai et al, 2013;Lin et al, 2014), these approaches should be further studied in a global scope, which will be contributed by this hybrid cloud. Furthermore, the basic philosophy of IN2CLOUD comes from Bayesian learning, wherein all related data/information or knowledge from others become "prior" information.…”

“…Information from failure reports on CM needs to be included as well. A maintenance threshold based on safety thresholds and maintenance capability is also part of the PM strategy for re-profiling (Lin et al, 2014;; such thresholds should be considered in diagnostic modeling because wheel profile data collected from condition monitoring sensors, such as wayside performance measurement systems, stem from condition-based maintenance (CBM) work orders (Asplund and Lin, 2016).…”

“…Local maintenance strategies will be optimized over time by using the Bayesian learning approach (Lin et al, 2014;; for example, prior information can be updated and knowledge and models can be improved. Defined asset information (e.g., asset information, operation information, and other maintenance information) and maintenance information from the private cloud (e.g., faulty analysis, degradation analysis, diagnostics, and prognostics) will be shared in the community cloud.…”

Analysis for locomotive wheels' degradation

“…However, in previous studies, some researchers have noticed that, the wheels' different installed positions could influence the results [9][10][11]. Recently, to solve the combined problem of small data samples and incomplete datasets while simultaneously considering the influence of several covariates, Lin et al [11] has explored the influence of locomotive wheels' positioning on reliability with Bayesian parametric models. Their results indicate that the particular bogie in which the wheel is mounted has more influence on its lifetime than does the axel or which side it is on.…”

“…Seeking to optimize this maintenance strategy, researchers have examined wheel degradation data to determine wheel reliability and failure distribution. However, in previous studies, some researchers have noticed that, the wheels' different installed positions could influence the results [9][10][11]. Recently, to solve the combined problem of small data samples and incomplete datasets while simultaneously considering the influence of several covariates, Lin et al [11] has explored the influence of locomotive wheels' positioning on reliability with Bayesian parametric models.…”