Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation
In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.
prognostics and health management (PHM), predictive maintenance, PHM approaches, Prognostics tools evaluation, Bogie components monitoring, PHM of Bogie, system-level PHM, component-level PHM
An, D., Choi, J.-H., & Kim, N. H. (2013). Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab. Reliability Engineering & System Safety, 115, 161–169.
An, D., Kim, N. H., & Choi, J. H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering and System Safety, 133, 223–236.
Andre, D., Appel, C., Soczka-Guth, T., & Sauer, D. U. (2013). Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries. Journal of Power Sources.
Atamuradov, V., & Camci, F. (2016). Evaluation of Features with Changing Effectiveness for Prognostics. Annual Conference of the Prognostics and Health Management Society 2016.
Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2013). Ensemble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, 41(1–2), 288–300.
Baraldi, P., Mangili, F., & Zio, E. (2015). A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression. Progress in Nuclear Energy, 78, 141–154.
Boukra, T., & Lebaroud, A. (2014). Identifying New Prognostic Features for Remaining Useful Life Prediction. Power Electronics and Motion Control Conference and Exposition (PEMC), 2014 16th International, 1216–1221.
Boutsidis, C., & Garber, D. (2015). Online Principal Component Analysis. Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms., 887–901.
Bressel, M., Hilairet, M., Hissel, D., & Ould Bouamama, B. (2016). Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell. Applied Energy, 164, 220–227.
Burgess, W. L. (2009). Valve Regulated Lead Acid battery float service life estimation using a Kalman filter. Journal of Power Sources, 191(1), 16–21.
Camci, F., Eker, O. F., Baskan, S., & Konur, S. (2016). Comparison of sensors and methodologies for effective prognostics on railway turnout systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit.
Campbell, G. S., & Lahey, R. (1984). A survey of serious aircraft accidents involving fatigue fracture. International Journal of Fatigue, 6(1), 25–30.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40(1), 16–28.
Choi, Y., & Liu, C. R. (2007). Spall progression life model for rolling contact verified by finish hard machined surfaces. Wear, 262(1–2), 24–35.
Coble, J. B., Ramuhalli, P., Bond, L. J., Hines, W., & Upadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power plants. International Journal of Prognostics and Health Management, 6(SP3), 1–22.
Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. Proceedings of the Annual Conference of the Prognostics and Health Management Society, 1–11.
Cremona, M. A., Liu, B., Hu, Y., Bruni, S., & Lewis, R. (2016). Predicting railway wheel wear under uncertainty of wear coefficient, using universal kriging. Reliability Engineering and System Safety, 154, 49–59.
Daigle, M., Sankararaman, S., & Roychoudhury, I. (2016). System-level Prognostics for the National Airspace. Annual Conference of the Prognostics and Health Management Society 2016, 1–9.
Ding, X. J., & Mei, T. X. (2008). Fault Detection for Vehicle Suspensions Based on System Dynamic Interactions. Procedings of the UKACC International Conference on Control. Retrieved from
Dong, H., Jin, X., Lou, Y., & Wang, C. (2014). Lithium-ion Battery State of health monitoring and Remaining Useful Life prediction based on Support Vector Regression-Particle Filter. Journal of Power Sources, 271, 114–123.
Dong, M., & He, D. (2007). A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing, 21(5), 2248–2266.
Duong, P. L. T., & Raghavan, N. (2017). Uncertainty quantification in prognostics: A data driven polynomial chaos approach. 2017 IEEE International Conference on Prognostics and Health Management, ICPHM 2017, 135–142.
Eker, O. F., Camci, F., Guclu, A., Yilboga, H., Sevkli, M., & Baskan, S. (2011). A simple state-based prognostic model for railway turnout systems. IEEE Transactions on Industrial Electronics, 58(5), 1718–1726.
Eker, O. F., Camci, F., & Jennions, I. K. (2015). Physics-based prognostic modelling of filter clogging phenomena. Mechanical Systems and Signal Processing, 75, 395–412.
Ferri, F. A. S., Rodrigues, L. R., Gomes, J. P. P., de Medeiros, I. P., Galvao, R. K. H., & Nascimento, C. L. (2013). Combining PHM information and system architecture to support aircraft maintenance planning. 2013 IEEE International Systems Conference (SysCon), 60–65.
Forman, R. G. (1972). Study of fatigue crack initiation from flaws using fracture mechanics theory. Engineering Fracture Mechanics, 4(2), 333–345.
Gebraeel, N., Lawley, M., Liu, R., & Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: A neural network approach. IEEE Transactions on Industrial Electronics, 51(3), 694–700.
Goebel, K., Eklund, N., & Bonanni, P. (2006). Fusing competing prediction algorithms for prognostics. 2006 IEEE Aerospace Conference.
Gu, J., Vichare, N., Ayyub, B., & Pecht, M. (2010). Application of Grey Prediction Model for Failure Prognostics of Electronics. International Journal of Performability Engineering, 6(5), 435–442.
Guan, X., Giffin, A., Jha, R., & Liu, Y. (2012). Maximum relative entropy-based probabilistic inference in fatigue crack damage prognostics. Probabilistic Engineering Mechanics, 29, 157–166.
Guillén, A. J., Gómez, J. F., Crespo, A., Guerrerro, A., Sola, A., & Barbera, L. (2013). Advances in PHM application frameworks: Processing methods, prognosis models, decision making. Chemical Engineering Transactions, 33, 391–396.
Halis Yilboga, Ömer Faruk Eker, A. G. F. C. (2010). Failure Prediction on Railway Turnouts Using TDNN_. 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.
He, J., Lu, Z., & Liu, Y. (2012). New Method for Concurrent Dynamic Analysis and Fatigue Damage Prognosis of Bridges. Journal of Bridge Engineering, 17(3), 396–408.
He, W., Williard, N., Osterman, M., & Pecht, M. (2011). Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 196(23), 10314–10321.
Henao, H., Kia, S. H., & Capolino, G. A. (2011). Torsional-vibration assessment and gear-fault diagnosis in railway traction system. IEEE Transactions on Industrial Electronics, 58(5), 1707–1717.
Hendricks, C., Williard, N., Mathew, S., & Pecht, M. (2015). A failure modes, mechanisms, and effects analysis (FMMEA) of lithium-ion batteries. Journal of Power Sources, 297, 113–120.
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.
Hong-feng, W. (2012). Prognostics and Health Management for Complex system Based on Fusion of Model-based approach and Data-driven approach. Physics Procedia, 24, 828–831.
Hong, M., Wang, Q., Su, Z., & Cheng, L. (2014). In situ health monitoring for bogie systems of CRH380 train on Beijing-Shanghai high-speed railway. Mechanical Systems and Signal Processing, 45(2), 378–395.
Hu, C., Youn, B. D., Wang, P., & Taek Yoon, J. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering and System Safety, 103, 120–135.
Huang, T., Wang, L., Jiang, T., & District, H. (2010). Prognostics of Products Using Time Series Analysis Based on Degradation Data. 2010 Prognostics and System Health Management Conference, Macao.
Irwin, G. R., & Paris, P. C. (1971). CHAPTER 1 – FUNDAMENTAL ASPECTS OF CRACK GROWTH AND FRACTURE. In Engineering Fundamentals and Environmental Effects (pp. 1–46).
ISO 13372:2012. (n.d.). Condition monitoring and diagnostics of machines-Vocabulary. Retrieved from http://viewer.afnor.org/Pdf/Viewer/?token=VR4VvnprAPA1
ISO 13374-1:2003. (n.d.). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 1: General guidelines. Retrieved from http://viewer.afnor.org/Pdf/Viewer/?token=CBE-mBI2g7w1
ISO 13381-1:2005. (n.d.). Condition monitoring and diagnostics of machines — Prognostics — Part 1: General guidelines. Retrieved from http://viewer.afnor.org/Pdf/Viewer/?token=ou_CxV4KW2s1
ISO 17359:2011. (n.d.). Condition monitoring and diagnostics of machines- General guidelines. Retrieved from http://viewer.afnor.org/Pdf/Viewer/?token=35DEUCJIWQI1
J.Z. Sikorska , M. Hodkiewiczb, L. M. (2011). Prognostic modelling options for remaining useful life estimation.pdf. Mechanical Systems and Signal Processing 25 (2011) 1803–1836 Contents.
Jardine, A. K. S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.
Javed, K., Gouriveau, R., Zemouri, R., & Zerhouni, N. (2011). Improving data-driven prognostics by assessing predictability of features. Annual Conference of the Prognostics and Health Management Society, 1–6.
Jouin, M., Gouriveau, R., Hissel, D., Péra, M. C., & Zerhouni, N. (2014). Prognostics of PEM fuel cell in a particle filtering framework. International Journal of Hydrogen Energy, 39(1), 481–494.
Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35.
Kan, M. S., Tan, A. C. C., & Mathew, J. (2015). A review on prognostic techniques for non-stationary and non-linear rotating systems. Mechanical Systems and Signal Processing, 62, 1–20.
Kandukuri, S. T., Klausen, A., Karimi, H. R., & Robbersmyr, K. G. (2016). A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews, 53, 697–708.
Khorasgani, H., Biswas, G., & Sankararaman, S. (2016). Methodologies for system-level remaining useful life prediction. Reliability Engineering and System Safety, 154, 8–18.
Kimotho, J. K., & Sextro, W. (2014). An approach for feature extraction and selection from non-trending data for machinery prognosis. Proceedings of the Second European Conference of the Prognostics and Health Management Society, 1–8.
Kumar, S., Torres, M., Chan, Y. C., & Pecht, M. (2008). A hybrid prognostics methodology for electronic products. Proceedings of the International Joint Conference on Neural Networks, 3479–3485.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems - Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1–2), 314–334.
Lee, S., Cui, H., Rezvanizaniani, M., & Ni, J. (2012). Battery Prognostics: Soc and Soh Prediction. Proceedings of the ASME 2012 International Manufacturing Science and Engineering Conference, 1–7.
Li, B., Zhang, P. L., Tian, H., Mi, S. S., Liu, D. S., & Ren, G. Q. (2011). A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox. Expert Systems with Applications, 38(8), 10000–10009.
Li, C. J., & Lee, H. (2005). Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 19(4), 836–846.
Li, H., Zhao, J., Yang, R., Zhao, J., & Teng, H. (2014). Research on planetary gearboxes feature selection and fault diagnosis based on EDT and FDA. Proceedings of 2014 Prognostics and System Health Management Conference, PHM 2014, 178–181.
LI, Y., KURFESS, T. R., & LIANG, S. Y. (2000). Stochastic Prognostics for Rolling Element Bearings. Mechanical Systems and Signal Processing, 14(5), 747–762.
Liang, S. Y., Li, Y., Billington, S. A., Zhang, C., Shiroishi, J., Kurfess, T. R., & Danyluk, S. (2014). Adaptive prognostics for rotary machineries. Procedia Engineering, 86, 852–857.
Liao, L. (2014). Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Transactions on Industrial Electronics, 61(5), 2464–2472.
Liao, L., & Köttig, F. (2014). Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Transactions on Reliability, 63(1), 191–207.
Liao, L., & Köttig, F. (2016). A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction. Applied Soft Computing Journal, 44, 191–199.
Lim, C. K. R., & Mba, D. (2015). Switching Kalman filter for failure prognostic. Mechanical Systems and Signal Processing, 52–53(1), 426–435.
Ling, L., Xiao, X., Xiong, J., Zhou, L., Wen, Z., & Jin, X. (2014). A 3D model for coupling dynamics analysis of high-speed train/track system. Journal of Zhejiang University SCIENCE A, 15(12), 964–983.
Liu, D., Pang, J., Zhou, J., Peng, Y., & Pecht, M. (2013). Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression. Microelectronics Reliability, 53(6), 832–839.
Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N., & Lee, J. (2007). Similarity based method for manufacturing process performance prediction and diagnosis. Computers in Industry, 58(6), 558–566.
Liu, J., Zhang, M., Zuo, H., & Xie, J. (2014). Remaining useful life prognostics for aeroengine based on superstatistics and information fusion. Chinese Journal of Aeronautics, 27(5), 1086–1096.
Liu, J., & Zio, E. (2016). System dynamic reliability assessment and failure prognostics. Reliability Engineering & System Safety.
Liu, K., Gebraeel, N. Z., & Shi, J. (2013). A Data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 10(3), 652–664.
Liu, L., Wang, S., Liu, D., Zhang, Y., & Peng, Y. (2015). Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine. Microelectronics Reliability, 55(9–10), 2092–2096.
Long, B., Xian, W., Jiang, L., & Liu, Z. (2013). An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectronics Reliability, 53(6), 821–831.
Marble, S., & Morton, B. P. (2006). Predicting the Remaining Life of Propulsion System Bearings. 2006 IEEE Aerospace Conference, 1–8.
Marine Jouin n, Rafael Gouriveau, Daniel Hissel, Marie-Cécile Péra, N. Z. (2016). Particle filter-based prognostics.Review, discussion and perspectives.
Mathew, S., Alam, M., & Pecht, M. (2012). Identification of Failure Mechanisms to Enhance Prognostic Outcomes. Journal of Failure Analysis and Prevention, 12(1), 66–73.
Mei, T. X., & Ding, X. J. (2008). New condition monitoring techniques for vehicle suspensions. Railway Condition Monitoring, 2008 4th IET International Conference on, 1–6.
Melnik, R., Sowi, B., Melnik, R., & Sowi, B. (2014). The Selection Procedure of Diagnostic Indicator of Suspension Fault Modes for the Rail Vehicles Monitoring. EWSHM - 7th European Workshop on Structural Health Monitoring, 159–166.
Mohamed Daowd, Noshin Omar, Bavo Verbrugge, Peter Van Den Bossche, J. V. M. (2010). Battery Models Parameter Estimation based on Matlab : Simulink. The 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium & Exhibition, 2.
Morgado, T. L. M., Branco, C. M., & Infante, V. (2008). A failure study of housing of the gearboxes of series 2600 locomotives of the Portuguese Railway Company. Engineering Failure Analysis, 15(1–2), 154–164.
Niu, G., & Yang, B. S. (2010). Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Systems with Applications, 37(12), 8831–8840.
Orsagh, R. F., Sheldon, J., & Klenke, C. J. (2003). Prognostics/diagnostics for gas turbine engine bearings. IEEE Aerospace Conference Proceedings, 7, 3095–3103.
Paris, P., & Erdogan, F. (1963). A Critical Analysis of Crack Propagation Laws. Journal of Basic Engineering, 85(4), 528.
Peng, Y., & Dong, M. (2011). A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets. Expert Systems with Applications, 38(10), 12946–12953.
Pennacchi, P., Chatterton, S., Vania, A., Ricci, R., & Borghesani, P. (2013). Experimental evidences in bearing diagnostics for traction system of high speed trains. Chemical Engineering Transactions, 33, 739–744.
Pennacchi, P., & Vania, A. (2008). Diagnostics of a crack in a load coupling of a gas turbine using the machine model and the analysis of the shaft vibrations. Mechanical Systems and Signal Processing, 22(5), 1157–1178.
Pillai, P., Kaushik, A., Bhavikatti, S., Roy, A., & Kumar, V. (2016). A Hybrid Approach for Fusing Physics and Data for Failure Prediction. International Journal of Prognostics and Health Management, 2153–2648.
Piyush Tagade a, Krishnan S. Hariharan a, Priya Gambhire a, S. M. K. a, & Taewon Song b, Dukjin Oh b, Taejung Yeo b, S. D. b. (2016). Recursive Bayesian filtering framework for lithium-ion cell state estimation. Journal of Power Sources.
Qiao, G., & Weiss, B. A. (2016). Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics. International Journal of Prognostics and Health Management, 7(Spec Iss on Smart Manufacturing PHM).
Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014). Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility. Journal of Power Sources, 256, 110–124.
Sai Sarathi Vasan, A., Chen, C., & Pecht, M. (2013). A Circuit-Centric Approach to Electronic System- Level Diagnostics and Prognostics. Prognostics and Health Management (PHM), 2013 IEEE Conference on, 1–8.
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52–53(1), 228–247.
Sankararaman, S., & Goebel, K. (2013). A Novel Computational Methodology for Uncertainty Quantification in Prognostics Using The Most Probable Point Concept. Annual Conference of the Prognostics and Health Management Society 2013, 1–13.
Sankavaram, C., Kodali, A., Pattipati, K., Singh, S., Zhang, Y., & Salman, M. (2016). An Inference-based Prognostic Framework for Health Management of Automotive Systems. International Journal of Prognostics and Health Management, 2153–2648.
Satish, B., & Sarma, N. D. R. (2005). A fuzzy BP approach for diagnosis and prognosis of bearing faults in induction motors. IEEE Power Engineering Society General Meeting 2005, 1–4.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). Evaluating algorithm performance metrics tailored for prognostics. IEEE Aerospace Conference Proceedings.
Shahidi, P., Maraini, D., Hopkins, B., & Seidel, A. (2014). Estimation of Bogie Performance Criteria Through On-Board Condition Monitoring. International Journal of Prognostics and Health Management, 1–10.
Shahidi, P., Maraini, D., Hopkins, B., & Seidel, A. (2015). Railcar Bogie Performance Monitoring using Mutual Information and Support Vector Machines. Annual Conference of the Prognostics and Health Management Society, 1–10.
Sharma, V., & Parey, A. (2016). A Review of Gear Fault Diagnosis Using Various Condition Indicators. Procedia Engineering, 144, 253–263.
Skarlatos, D., Karakasis, K., & Trochidis, A. (2004). Railway wheel fault diagnosis using a fuzzy-logic method. Applied Acoustics, 65(10), 951–966.
Skima, H., Medjaher, K., Varnier, C., Dedu, E., & Bourgeois, J. (2016a). A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation. Microelectronics Reliability, 65, 79–88.
Skima, H., Medjaher, K., Varnier, C., Dedu, E., & Bourgeois, J. (2016b). A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation. Microelectronics Reliability.
Sun, J., Zuo, H., Wang, W., & Pecht, M. G. (2014). Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model. Mechanical Systems and Signal Processing, 45(2), 396–407.
Swanson, D. C. (2001). A general prognostic tracking algorithm for predictive maintenance. 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542), 6, 2971–2977.
Symonds, N., Corni, I., Wood, R. J. K., Wasenczuk, A., & Vincent, D. (2015). Observing early stage rail axle bearing damage. Engineering Failure Analysis, 56, 216–232.
Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). CNC machine tools wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing, 28, 167–182.
Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012). A data-driven failure prognostics method based on mixture of Gaussian hidden Markov models. IEEE Transactions on Reliability, 61(2), 491–503.
Tsunashima, H., & Mori, H. (2010). Condition monitoring of railway vehicle suspension using adaptive multiple model approach. Control Automation and Systems (ICCAS), 2010 International Conference on, 584–589.
Virkler, D. A., Hillberry, B. M., & Goel, P. K. (1979). The Statistical Nature of Fatigue Crack Propagation. Journal of Engineering Materials and Technology, 101(2), 148.
Wang, P., Youn, B. D., & Hu, C. (2012). A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing, 28, 622–637.
Wang, Q., Su, Z., & Hong, M. (2014). Online Damage Monitoring for High-Speed Train Bogie Using Guided Waves: Development and Validation. 7th European Workshop on Structural Health Monitoring July 8-11, 2014. La Cité, Nantes, France.
Wei, C., Xin, Q., Chung, W. H., Liu, S. Y., Tam, H. Y., & Ho, S. L. (2012). Real-time train wheel condition monitoring by fiber Bragg grating sensors. International Journal of Distributed Sensor Networks, 2012.
Wei Wu, J. H. and J. Z. (2007). Prognostics of Machine Health Condition using an Improved ARIMA-based Prediction method. 2007 2nd IEEE Conference on Industrial Electronics and Applications, 1062–1067.
Williard, N., He, W., Osterman, M., & Pecht, M. (2013). Comparative analysis of features for determining state of health in lithium-ion batteries. Int. J. Progn. Health Manag, 2013(4), 1–7.
Wu, Y., Jiang, B., Lu, N., & Zhou, D. (2015). ToMFIR-based Incipient Fault Detection and Estimation for High-speed Rail Vehicle Suspension System. Journal of the Franklin Institute, 352(4), 1672–1692.
Xie, G., Ye, M., Hei, X., Zhao, J., & Qian, F. (2015). Data-Based Health State Analysis for the Axle of High Speed Train. 2015 11th International Conference on Computational Intelligence and Security (CIS), 454–457.
Yang, W. A., Xiao, M., Zhou, W., Guo, Y., & Liao, W. (2016). A hybrid prognostic approach for remaining useful life prediction of lithium-ion batteries. Shock and Vibration, 2016.
Yi, C., Lin, J., Zhang, W., & Ding, J. (2015). Faults diagnostics of railway axle bearings based on IMF’s confidence index algorithm for ensemble EMD. Sensors, 15(5), 10991–11011.
Yu, J. (2011). Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems and Signal Processing, 25(7), 2573–2588.
Zerbst, U., Beretta, S., Kohler, G., Lawton, A., Vormwald, M., Beier, H. T., … Klingbeil, D. (2013). Safe life and damage tolerance aspects of railway axles - A review. Engineering Fracture Mechanics, 98(1), 214–271.
Zhang, B., Tan, A. C. C., & Lin, J. hui. (2016). Gearbox fault diagnosis of high-speed railway train. Engineering Failure Analysis, 66, 407–420.
Zhang, G. (2005). Optimum Sensor Localization/Selection In A Diagnostic/Prognostic Architecture. Georgia Institute of Technology, Dissertation, (January).
Zhao, F., Tian, Z., & Zeng, Y. (2013). Uncertainty quantification in gear remaining useful life prediction through an integrated prognostics method. IEEE Transactions on Reliability, 62(1), 146–159.
Zhu, J., Nostrand, T., Spiegel, C., & Morton, B. (2014). Survey of Condition Indicators for Condition Monitoring Systems. Annual Conference of the Prognostics and Health Management Society, 5, 1–13.
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403–409.