Abstract
Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.
Highlights
Research ArticleReceived 25 June 2019; Revised 6 February 2020; Accepted 13 February 2020; Published 11 March 2020
Many freight trains for special lines have in common the characteristics of a fixed group
The wheel status will directly affect the train operation quality and safety, as an important part of any railway freight car. e wheel tread wear is one of the key parameters reflecting the wheel state in relation to the service time. e Remaining Useful Life (RUL) of wheels can be predicted using a degradation model based on offline history tread wear data and online monitoring data, serving as an important basis for Mathematical Problems in Engineering vehicle Centralized Condition-Based Maintenance (CCBM). e dispersion of tread wear data for different wheels will gradually increase over operation time, producing different degradation curves for each individual wheel
Summary
Received 25 June 2019; Revised 6 February 2020; Accepted 13 February 2020; Published 11 March 2020. A prediction algorithm of individual RUL, based on nonlinear Wiener process, is proposed for the prediction of centralized maintenance time of railway freight train wheels. HP filter is used to process the monitoring data of wheel tread wear, while the trend component of wheel wear degradation is extracted for model parameters estimation and individual RUL prediction. E degradation trend data of wheel tread wear, after HP filtering, is used to establish the Wiener process model, as described . The degradation data of all the wheels on the same train is taken as a sample set, where the overall model parameter θ is estimated, according to the maximum likelihood estimation method. Taking the 500-day monitoring data of tread wear, in a 54 fixed group of railway freight cars, as an example, based
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