Abstract

This paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modeled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled, and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.

References

1.
Ghofrani
,
F.
,
He
,
Q.
,
Goverde
,
R. M. P.
, and
Liu
,
X.
,
2018
, “
Recent Applications of Big Data Analytics in Railway Transportation Systems: A Survey
,”
Transp. Res. Part C
,
90
, pp.
226
246
.10.1016/j.trc.2018.03.010
2.
Famurewa
,
S. M.
,
Zhang
,
L.
, and
Asplund
,
M.
,
2017
, “
Maintenance Analytics for Railway Infrastructure Decision Support
,”
J. Qual. Maint. Eng.
,
23
(
3
), pp.
310
325
.10.1108/JQME-11-2016-0059
3.
Tattini
,
J.
, and
Teter
,
J.
,
2020
, “
Rail,” International Energy Agency, accessed Aug. 15, 2021, https://www.iea.org/reports/rail
4.
Armstrong
,
J.
,
Preston
,
J.
, and
Hood
,
I.
,
2019
, “
Developing an Operational Philosophy for Britain's Railways
,”
World Conference on Transport Research—WCTR
,
Mumbai, India
, May
26
31
.
6.
Bevan
,
A.
,
Molyneux-Berry
,
P.
,
Mills
,
S.
,
Rhodes
,
A.
, and
Ling
,
D.
,
2013
, “
Optimisation of Wheelset Maintenance Using Whole-System Cost Modeling
,”
Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit
,
227
(
6
), pp.
594
608
.10.1177/0954409713484712
7.
Iwnicki
,
S.
,
2006
,
Handbook of Railway Vehicle Dynamics
,
Taylor & Francis Group
,
Boca Raton, FL
.
8.
O'Connor
,
P. D. T.
, and
Kleyner
,
A.
,
2011
,
Practical Reliability Engineering
,
Wiley
,
Chichester, UK
.
9.
Andrade
,
A. R.
, and
Stow
,
J.
,
2016
, “
Statistical Modeling of Wear and Damage Trajectories of Railway Wheelsets
,”
Qual. Reliab. Eng. Int.
,
32
(
8
), pp.
2909
2923
.10.1002/qre.1977
10.
Shebani
,
A.
, and
Iwnicki
,
S.
,
2018
, “
Prediction of Wheel and Rail Wear Under Different Contact Conditions Using Artificial Neural Networks
,”
Wear
,
406–407
, pp.
173
184
.10.1016/j.wear.2018.01.007
11.
Braghin
,
F.
,
Lewis
,
R.
,
Dwyer-Joyce
,
R. S.
, and
Bruni
,
S.
,
2006
, “
A Mathematical Model to Predict Railway Wheel Profile Evolution Due to Wear
,”
Wear
,
261
(
11–12
), pp.
1253
1264
.10.1016/j.wear.2006.03.025
12.
Ignesti
,
M.
,
Innocenti
,
A.
,
Marini
,
L.
,
Meli
,
E.
, and
Rindi
,
A.
,
2014
, “
Development of a Model for the Simultaneous Analysis of Wheel and Rail Wear in Railway Systems
,”
Multibody Syst. Dyn.
,
31
(
2
), pp.
191
240
.10.1007/s11044-013-9360-0
13.
Butini
,
E.
,
Marini
,
L.
,
Meacci
,
M.
,
Meli
,
E.
,
Rindi
,
A.
,
Zhao
,
X. J.
, and
Wang
,
W. J.
,
2019
, “
An Innovative Model for the Prediction of Wheel—Rail Wear and Rolling Contact Fatigue
,”
Wear
,
436–437
, p.
203025
.10.1016/j.wear.2019.203025
14.
Pombo
,
J.
,
Ambrósio
,
J.
,
Pereira
,
M.
,
Lewis
,
R.
,
Dwyer-Joyce
,
R.
,
Ariaudo
,
C.
, and
Kuka
,
N.
,
2011
, “
Development of a Wear Prediction Tool for Steel Railway Wheels Using Three Alternative Wear Functions
,”
Wear
,
271
(
1–2
), pp.
238
245
.10.1016/j.wear.2010.10.072
15.
Palo
,
M.
,
Galar
,
D.
,
Nordmark
,
T.
,
Asplund
,
M.
, and
Larsson
,
D.
,
2014
, “
Condition Monitoring at the Wheel/Rail Interface for Decision-Making Support
,”
Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit
,
228
(
6
), pp.
705
715
.10.1177/0954409714526164
16.
Lin
,
J.
,
Asplund
,
M.
, and
Nordmark
,
T.
,
2015
, “
Data Analysis of Wheel-Sets' Running Surface Wear Based on Re-Profiling Measurement: A Case Study at Malmbanan
,”
IHHA Conference
,
Perth, Australia
, June 21–24https://www.researchgate.net/publication/290937847_DATA_ANALYSIS_OF_WHEELSETS'_RUNNING_SURFACE_WEAR_BASED_ON_RE-PROFILING_MEASUREMENT_A_CASE_STUDY_AT_MALMBANAN.
17.
Lasisi
,
A.
, and
Attoh-Okine
,
N.
,
2019
, “
Machine Learning Ensembles and Rail Defects Prediction: Multilayer Stacking Methodology
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng.
,
5
(
4
), p.
04019016
.10.1061/AJRUA6.0001024
18.
Martey
,
E. N.
,
Ahmed
,
L.
, and
Attoh-Okine
,
N.
,
2017
, “
Track Geometry Big Data Analysis: A Machine Learning Approach
,”
IEEE International Conference on Big Data
,
IEEE
,
Boston, MA
, Dec. 11–14, pp.
3800
3809
.10.1109/BigData.2017.8258381
19.
Wang
,
L.
,
Xu
,
H.
,
Yuan
,
H.
,
Zhao
,
W.
, and
Chen
,
X.
,
2015
, “
Optimizing the Re-Profiling Strategy of Metro Wheels Based on a Data-Driven Wear Model
,”
Eur. J. Oper. Res.
,
242
(
3
), pp.
975
986
.10.1016/j.ejor.2014.10.033
20.
Zhu
,
W.
,
Yang
,
D.
,
Guo
,
Z.
,
Huang
,
J.
, and
Huang
,
Y.
,
2015
, “
Data-Driven Wheel Wear Modeling and Reprofiling Strategy Optimization for Metro Systems
,”
Transp. Res. Rec. J. Transp. Res. Board
,
2476
(
1
), pp.
67
75
.10.3141/2476-10
21.
Lin
,
J.
,
Asplund
,
M.
, and
Parida
,
A.
,
2014
, “
Reliability Analysis for Degradation of Locomotive Wheels Using Parametric Bayesian Approach
,”
Qual. Reliab. Eng. Int.
,
30
(
5
), pp.
657
667
.10.1002/qre.1518
22.
Chi
,
Z.
,
Lin
,
J.
,
Chen
,
R.
, and
Huang
,
S.
,
2020
, “
Data-Driven Approach to Study the Polygonization of High-Speed Railway Train Wheel-Sets Using Field Data of China's HSR Train
,”
Meas. J. Int. Meas. Confed.
,
149
, p.
107022
.10.1016/j.measurement.2019.107022
23.
Jiang
,
Z.
,
Banjevic
,
D.
,
Mingcheng
,
E. M.
, and
Li
,
B.
,
2017
, “
Optimizing the Re-Profiling Policy Regarding Metropolitan Train Wheels Based on a Semi-Markov Decision Process
,”
Proc. Inst. Mech. Eng. Part O J. Risk Reliab.
,
231
(
5
), pp.
495
507
.10.1177/1748006X17710816
24.
Braga
,
J. A. P.
, and
Andrade
,
A. R.
,
2019
, “
Optimizing Maintenance Decisions in Railway Wheelsets: A Markov Decision Process Approach
,”
Proc. Inst. Mech. Eng. Part O J. Risk Reliab.
,
233
(
2
), pp.
285
300
.10.1177/1748006X18783403
25.
Mingcheng
,
E.
,
Li
,
B.
,
Jiang
,
Z.
, and
Li
,
Q.
,
2018
, “
An Optimal Reprofiling Policy for High-Speed Train Wheels Subject to Wear and External Shocks Using a Semi-Markov Decision Process
,”
IEEE Trans. Reliab.
,
67
(
4
), pp.
1468
1481
.10.1109/TR.2018.2870276
26.
Costa
,
M. A.
,
Braga
,
J. A. P.
, and
Andrade
,
A. R.
,
2021
, “
A Data-Driven Maintenance Policy for Railway Wheelset Based on Survival Analysis and Markov Decision Process
,”
Qual. Reliab. Eng. Int.
,
37
(
1
), pp.
176
198
.10.1002/qre.2729
27.
Schöbi
,
R.
, and
Chatzi
,
E. N.
,
2016
, “
Maintenance Planning Using Continuous-State Partially Observable Markov Decision Processes and Non-Linear Action Models
,”
Struct. Infrastruct. Eng.
,
12
(
8
), pp.
977
994
.10.1080/15732479.2015.1076485
28.
Kaplan
,
E. L.
, and
Meier
,
P.
,
1958
, “
Nonparametric Estimation From Incomplete Observations
,”
J. Am. Stat. Assoc.
,
53
(
282
), pp.
457
481
.10.1080/01621459.1958.10501452
29.
Kleinbaum
,
D.
, and
Klein
,
M. G.
,
2005
,
Survival Analysis: A Self-Learning Text
,
Springer
,
New York
.
30.
Costa
,
M. A.
,
Braga
,
J. A. P.
, and
Andrade
,
A. R.
,
2020
, “
Assessing the Performance of Different Devices in Railway Wheelset Inspection
,”
Measurement
,
165
(
108145
), p.
108145
.10.1016/j.measurement.2020.108145
31.
Asplund
,
M.
, and
Lin
,
J.
,
2016
, “
Evaluating the Measurement Capability of a Wheel Profile Measurement System by Using GR & R
,”
Meas.
,
92
, pp.
19
27
.10.1016/j.measurement.2016.05.090
32.
Braga
,
J. A.
, and
Andrade
,
A. R.
,
2021
, “
Multivariate Statistical Aggregation and Dimensionality Reduction Techniques to Improve Monitoring and Maintenance in Railways: The Wheelset Component
,”
Reliability Eng. Syst. Safety
,
216
, p.
107932
.10.1016/j.ress.2021.107932
33.
CEN, (European Committee for Standardization)
,
2013
,
Railway Applications—In-Service Wheelset Operation Requirements—In-Service and Off-Vehicle Wheelset Maintenance
,
British Standard Institution
,
London
, Standard No. EN 15313:2012.
34.
Andrade
,
A. R.
, and
Stow
,
J.
,
2017
, “
Assessing the Potential Cost Savings of Introducing the Maintenance Option of ‘Economic Tyre Turning’ in Great Britain Railway Wheelsets
,”
Reliab. Eng. Syst. Saf.
,
168
, pp.
317
325
.10.1016/j.ress.2017.05.033
35.
Delignette-Muller
,
M. L.
, and
Dutang
,
C.
,
2015
, “
Fitdistrplus: An R Package for Fitting Distributions
,”
J. Stat. Softw.
,
64
(
4
), pp.
1
34
.10.18637/jss.v064.i04
36.
Robert
,
C. P.
, and
Casella
,
G.
,
2004
,
Monte Carlo Statistical Methods
,
Springer
,
New York
.
37.
Shi
,
H.
,
Wang
,
J.
,
Wu
,
P.
,
Song
,
C.
, and
Teng
,
W.
,
2018
, “
Field Measurements of the Evolution of Wheel Wear and Vehicle Dynamics for High-Speed Trains
,”
Veh. Syst. Dyn.
,
56
(
8
), pp.
1187
1206
.10.1080/00423114.2017.1406963
38.
Sancho
,
L. C. B.
,
Braga
,
J. A. P.
, and
Andrade
,
A. R.
,
2021
, “
Optimizing Maintenance Decision in Rails;: A Markov Decision Process Approach
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng.
,
7
(
1
), p.
04020051
.10.1061/AJRUA6.0001101
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