Comparison of sensors and methodologies for effective prognostics on railway turnout systems

Railway turnout systems are one of the most important components of a railway’s infrastructure. Their geographically distributed nature makes failure detection, forecasting and maintenance planning extremely important. Prognostics, forecasting the time to failure in order to achieve effective maintenance planning, has attracted increasing attention from industry and researchers in recent years. The prognostic approach has great potential to achieve reduced costs and increased availability. However, the applicability of any engineering model requires economic and practical justifications. This paper presents an analysis of different prognostic methods for railway turnout systems. Five different sensors, installed in a real turnout system used on Turkish State Railways, are individually analysed by applying various prognostic methods. This paper aims to guide practitioners on the application of prognostics and health management technologies to railway turnout systems by discussing the advantages and disadvantages of using different sensors and prognostic methods.

[1]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[2]  Ying Peng,et al.  A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets , 2011, Expert Syst. Appl..

[3]  Tiedo Tinga,et al.  Application of physical failure models to enable usage and load based maintenance , 2010, Reliab. Eng. Syst. Saf..

[4]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[5]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[6]  Fatih Camci,et al.  State-Based Prognostics with State Duration Information , 2013, Qual. Reliab. Eng. Int..

[7]  Ratna Babu Chinnam,et al.  Health-State Estimation and Prognostics in Machining Processes , 2010, IEEE Transactions on Automation Science and Engineering.

[8]  Huiguo Zhang,et al.  A hybrid prognostics and health management approach for condition-based maintenance , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[9]  F. Camci,et al.  Dynamic Bayesian networks for machine diagnostics: hierarchical hidden Markov models vs. competitive learning , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[10]  Lei Zhang,et al.  A review of fault prognostics in condition based maintenance , 2006, International Symposium on Instrumentation and Control Technology.

[11]  C Roberts,et al.  Railway point mechanisms: Condition monitoring and fault detection , 2010 .

[12]  Ming Dong,et al.  Equipment PHM using non-stationary segmental hidden semi-Markov model , 2011 .

[13]  Yi-Qing Ni,et al.  Reliability-based condition assessment of in-service bridges using mixture distribution models , 2012 .

[14]  Rui Kang,et al.  Benefits and Challenges of System Prognostics , 2012, IEEE Transactions on Reliability.

[15]  Samuel H. Huang,et al.  System health monitoring and prognostics — a review of current paradigms and practices , 2006 .

[16]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[17]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[18]  Ying Peng,et al.  A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction , 2011 .

[19]  K. Goebel,et al.  Standardizing research methods for prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[20]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[21]  Clive Roberts,et al.  Life cycle costs for railway condition monitoring , 2008 .

[22]  Alaa Elwany,et al.  Residual Life Predictions in the Absence of Prior Degradation Knowledge , 2009, IEEE Transactions on Reliability.

[23]  Fatih Camci,et al.  Failure prediction on railway turnouts using time delay neural networks , 2010, 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

[24]  Mehmet Sevkli,et al.  A Simple State-Based Prognostic Model for Railway Turnout Systems , 2011, IEEE Transactions on Industrial Electronics.

[25]  Diego J. Pedregal,et al.  Unobserved Component models applied to the assessment of wear in railway points: A case study , 2007, Eur. J. Oper. Res..

[26]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[27]  Akio Hada,et al.  Lagrangian heuristic method for the wireless sensor network design problem in railway structural health monitoring , 2012 .

[28]  F Garciamarquez,et al.  A reliability centered approach to remote condition monitoring. A railway points case study , 2003 .

[29]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..