A Review: Prognostics and Health Management in Automotive and Aerospace

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jun 1, 2019
Van Duc Nguyen Marios Kefalas Kaifeng Yang Asteris Apostolidis Markus Olhofer Steffen Limmer Thomas B¨ack

Abstract

Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.

Abstract 1495 | PDF Downloads 7345

##plugins.themes.bootstrap3.article.details##

Keywords

prediction, prognostics, Remaining useful Life, prognostics and health management, Aerospace, Automotive

References
Abraham, A. (2005, July). Adaptation of Fuzzy Inference System Using Neural Learning. In J. Kacprzyk, N. Nedjah, & L. d. Macedo Mourelle (Eds.), Fuzzy
Systems Engineering (Vol. 181, pp. 53–83). Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/11339366_3
Accident: Southwest B737 near Philadelphia on Apr 17th 2018, uncontained engine failure takes out passenger window. (n.d.).
Ahmadzadeh, F., & Lundberg, J. (2014, December). Remaining useful life estimation: review. International Journal of System Assurance Engineering and Management, 5(4), 461–474. doi: 10.1007/s13198-013-0195-0
Alia, J. B., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on weibull distribution and artificial neural network. Mech Syst Signal Process, 150-172.
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50(2), 15.
Ashok, B., Denis Ashok, S., & Ramesh Kumar, C. (2016). A review on control system architecture of a SI engine management system. Annual Reviews in Control, 41, 94–118. doi: 10.1016/j.arcontrol.2016.04.005
Ashok, B., Denis Ashok, S., & Ramesh Kumar, C. (2017). Trends and future perspectives of electronic throttle control system in a spark ignition engine.
Annual Reviews in Control, 44, 97–115. doi: 10.1016/j.arcontrol.2017.05.002
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation. , 32.
Banghart, M., Bian, L., Strawderman, L., & Babski-Reeves, K. (2017, July). Risk assessment on the EA-6b aircraft utilizing Bayesian networks. Quality Engineering, 29(3), 499–511. doi: 10.1080/08982112.2017.1319957
Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento, C., Prendinger, H., & Henriques, E. M. (2018, January). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers & Industrial Engineering, 115, 41–53. doi: 10.1016/j.cie.2017.10.033
Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2013). Ensemble neural network-based particle filtering for prognostics. Mechanical Systems
and Signal Processing, 41(1), 288 - 300. doi: https://doi.org/10.1016/j.ymssp.2013.07.010
Beatrice, C., Guido, C., Napolitano, P., Iorio, S. D., & Giacomo, N. D. (2011, May). Assessment of biodiesel blending detection capability of the onboard
diagnostic of the last generation automotive diesel engines. Fuel, 90(5), 2039–2044. doi: 10.1016/j.fuel.2011.01.013
Bechhoefer, E., Bernhard, A., He, D., & Banerjee, P. (2006). Use of Hidden Semi-Markov Models in the Prognostics of Shaft Failure. , 7.
Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J., & Van den Bossche, P. (2016, April). Critical review of state of health estimation methods of Li-ion batteries for real applications. Renewable and Sustainable Energy Reviews, 56, 572–587. doi: 10.1016/j.rser.2015.11.042
Bolander, N., Qiu, H., Eklund, N., Hindle, E., & Rosenfeld, T. (2009). Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis. , 12.
Brier G.W., & Allen R.A. (1951). Verification of Weather Forecasts. Boston, MA: In: Malone T.F. (eds) Compendium of Meteorology. American Meteorological Society.
Byington, C., Watson, M., & Edwards, D. (2004). Datadriven neural network methodology to remaining life predictions for aircraft actuator components. In 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04th8720) (Vol. 6, pp. 3581–3589). Big Sky, MT, USA: IEEE. doi: 10.1109/AERO.2004.1368175
Campi, M., Lecchini, A., & Savaresi, S. (2002). Virtual reference feedback tuning: a direct method for the design of feedback controllers. Automatica, 38(8), 1337 - 1346. doi: https://doi.org/10.1016/S0005-1098(02)00032-8
Celaya, J. R., Saha, B., & Wysocki, P. F. (2009). Prognostics for Electronics Components of Avionics. , 7.
Chen, C., Vachtsevanos, G., & Orchard, M. E. (2012, April). Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and highorder
particle filtering approach. Mechanical Systems and Signal Processing, 28, 597–607. doi: 10.1016/j.ymssp.2011.10.009
Chen Xiongzi, Yu Jinsong, Tang Diyin, & Wang Yingxun. (2011, August). Remaining useful life prognostic estimation for aircraft subsystems or components: A review. In IEEE 2011 10th International Conference on Electronic Measurement & Instruments (pp. 94–98). Chengdu, China: IEEE. doi:
10.1109/ICEMI.2011.6037773
Cheng, S., Azarian, M. H., & Pecht, M. G. (2010, June). Sensor Systems for Prognostics and Health Management. Sensors, 10(6), 5774–5797. doi: 10.3390/s100605774
Coble, J., Ramuhalli, P., Bond, L. J., Hines, J. W., & Ipadhyaya, B. (2015). A Review of Prognostics and Health Management Applications in Nuclear Power Plants. , 24.
Company, T. (2006). Benefits of IVHM: An Analytical Approach. In 2006 IEEE Aerospace Conference (pp. 1–9). Big Sky, MT, USA: IEEE. doi:
10.1109/AERO.2006.1656072
Dang, X., Yan, L., Xu, K., Wu, X., Jiang, H., & Sun, H. (2016, January). Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion
Battery Using Dual Neural Network Fusion Battery Model. Electrochimica Acta, 188, 356–366. doi: 10.1016/j.electacta.2015.12.001
Daroogheh, N., Baniamerian, A., Meskin, N., & Khorasani, K. (2015, June). A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines. In 2015 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–8). Austin, TX, USA: IEEE. doi: 10.1109/ICPHM.2015.7245020
Daroogheh, N., Meskin, N., & Khorasani, K. (2013, June). Particle filtering for state and parameter estimation in gas turbine engine fault diagnostics. In 2013 American Control Conference (pp. 4343–4349). Washington, DC: IEEE. doi: 10.1109/ACC.2013.6580508
Daroogheh, N., Meskin, N., & Khorasani, K. (2014, June). A novel particle filter parameter prediction scheme for failure prognosis. In 2014 American Control Conference (pp. 1735–1742). Portland, OR, USA: IEEE. doi: 10.1109/ACC.2014.6859021
Dishant, E. S., Er.Parminder Singh. (2017, April). Suspension systems: A review. International Research Journal of Engineering and Technology (IRJET). doi: 10.1007/s00521-017-2986-8
Dong, M., & He, D. (2007). A segmental hidden semimarkov model (hsmm)-based diagnostics and prognostics framework and methodology. Mechanical Systems and Signal Processing, 21(5), 2248 - 2266. doi: https://doi.org/10.1016/j.ymssp.2006.10.001
Dong, M., He, D., Banerjee, P., & Keller, J. (2006, October). Equipment health diagnosis and prognosis using hidden semi-Markov models. The International Journal of Advanced Manufacturing Technology, 30(7-8), 738–749. doi: 10.1007/s00170-005-0111-0
dos Santos Coelho, L., Pessˆoa, M. W., Sumar, R. R., & Coelho, A. A. R. (2010). Model-free adaptive control design using evolutionary-neural compensator. Expert Systems with Applications, 37(1), 499 - 508. doi: https://doi.org/10.1016/j.eswa.2009.05.042
Downey, A., Lui, Y.-H., Hu, C., Laflamme, S., & Hu, S. (2019, February). Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds. Reliability Engineering & System Safety, 182, 1–12. doi: 10.1016/j.ress.2018.09.018
Dragomir, O. E., Gouriveau, R., Dragomir, F., Minca, E., & Zerhouni, N. (2009, August). Review of prognostic problem in condition-based maintenance. In 2009 European Control Conference (ECC) (pp. 1587–1592). Budapest: IEEE. doi: 10.23919/ECC.2009.7074633
Drappier, C. J. (2008). A380: Challenges for the Future. , 34.
Duan, L., Hou, Z., Yu, X., Jin, S., & Lu, K. (2019). Data-Driven Model-Free Adaptive Attitude Control Approach for Launch Vehicle With Virtual Reference
Feedback Parameters Tuning Method. IEEE Access, 7, 54106–54116. doi: 10.1109/ACCESS.2019.2912902
Ekwaro-Osire, Stephen, Alemayehu, Fisseha M, & Carlos Gonalves, Aparecido. (2017). Probabilistic Prognostics and Health Management of Energy Systems. Springer International Publishing.
Elattar, H. M., Elminir, H. K., & Riad, A. M. (2016, June). Prognostics: a literature review. Complex & Intelligent Systems, 2(2), 125–154. doi: 10.1007/s40747-016-0019-3
Ferreiro, S., & Arnaiz, A. (2010). Prognostics applied to aircraft line maintenance: Brake wear prediction based on Bayesian Networks. IFAC Proceedings Volumes, 43(3), 146–151. doi: 10.3182/20100701-2-PT-4012.00026
Fleming, W. (2001, December). Overview of automotive sensors. IEEE Sensors Journal, 1(4), 296–308. doi: 10.1109/7361.983469
Fliess, M., & Join, C. (2013). Model-free control. International Journal of Control, 86(12), 2228-2252. doi: 10.1080/00207179.2013.810345
Ghahramani, Z. (2001). AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS. Hidden Markov Models, 34.
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008, August). Prognostics in Battery Health Management. IEEE Instrumentation
& Measurement Magazine, 11(4), 33–40. doi: 10.1109/MIM.2008.4579269
Goebel, K., Saha, B., Saxena, A., & Field, M. (2008). A Comparison of Three Data-Driven Techniques For Prognostics. , 14.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., & Sun, Y. (2010). A review on degradation models in reliability analysis. In D. Kiritsis, C. Emmanouilidis, A. Koronios, & J. Mathew (Eds.), Engineering Asset Lifecycle Management (pp. 369–384). London: Springer London. doi: 10.1007/978-0-85729-320-642
Guo, Y., Hou, Z., Liu, S., & Jin, S. (2019). Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems With Stability Analysis. IEEE Access, 7, 102852–102866. doi: 10.1109/ACCESS.2019.2931198
He, W., Williard, N., Chen, C., & Pecht, M. (2014a). State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation. International Journal of Electrical Power and Energy Systems, 62, 783 - 791. doi: https://doi.org10.1016j.ijepes.2014.04.059
He, W., Williard, N., Chen, C., & Pecht, M. (2014b). State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation. International Journal of Electrical Power & Energy Systems, 62, 783 - 791. doi: https://doi.org10.1016j.ijepes.2014.04.059
Heimes, F. O. (2008, October). Recurrent neural networks for remaining useful life estimation. In 2008 International Conference on Prognostics and Health Management (pp. 1–6). Denver, CO, USA: IEEE. doi: 10.1109/PHM.2008.4711422
Hjalmarsson, H. (2002). Iterative feedback tuningan overview. International Journal of Adaptive Control and Signal Processing, 16(5), 373-395. doi:
10.1002/acs.714
Holzer,W. (2011). A380 Advanced Cabin Line Maintenance. , 41.
Hou, Z., & Jin, S. (2011, Nov). A novel data-driven control approach for a class of discrete-time nonlinear systems. IEEE Transactions on Control Systems Technology, 19(6), 1549-1558. doi: 10.1109/TCST.2010.2093136
Hou, Z., & Jin, S. (2014). Model Free Adaptive Control. , 59.
Howard, L., Mesick, J., Reuter, R., & Roemer, M. (2001). An Evolvable Tri-Reasoner IVHMl System. , 15.
IATA. (2018, June). Fact Sheet Fuel. IATA.
Incident: France A388 over Greenland on Sep 30th 2017, uncontained engine failure, fan and engine inlet separated. (n.d.).
International Civil Aviation Organization, C. A. S. o. t. W., & staff estimates., I. (n.d.). Air passengers carried include both domestic and international aircraft passengers of air carriers registered in the country. (Tech. Rep.).
Itier, J.-B. (2007). ARTIST2 IMA & ADCN. , 45.
Jafari, M., Khan, K.,&Gauchia, L. (2018, December). Deterministic models of Li-ion battery aging: It is a matter of scale. Journal of Energy Storage, 20, 67–77. doi: 10.1016/j.est.2018.09.002
Jaoude, A. A. (2015). Analytic and linear prognostic model for a vehicle suspension system subject to fatigue. Systems Science & Control Engineering, 3(1), 81-98. doi: 10.1080/21642583.2014.987359
Jardine, A. K., Lin, D., & Banjevic, D. (2006, October). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: 10.1016/j.ymssp.2005.09.012
Javed, G. R. Z. N., K. (2013, November). Novel failure prognostics approach with dynamic thresholds for machine degradation. In IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society (pp. 4404–4409). Vienna, Austria: IEEE. doi: 10.1109/IECON.2013.6699844
Jeong, J., Kim, N., Lim, W., Park, Y.-I., Cha, S. W., & Jang, M. E. (2017, October). Optimization of power management among an engine, battery and ultra-capacitor for a series HEV: A dynamic programming application. International Journal of Automotive Technology, 18(5), 891–900. doi: 10.1007/s12239-017-0087-4
Juesas, P., Ramasso, E., Drujont, S., & Placet, V. (2016). On partially supervised learning and inference in dynamic Bayesian networks for prognostics with uncertain factual evidence: Illustration with Markov switching models. , 10.
Jung, W., & Ismail, A. (2015). Prognostic and Health Management Trend in Automotive Industry: An Overview. , 7.
Kan, M. S., Tan, A. C., & Mathew, J. (2015, October). A review on prognostic techniques for nonstationary and non-linear rotating systems. Mechanical
Systems and Signal Processing, 62-63, 1–20. doi: 10.1016/j.ymssp.2015.02.016
Klabfleisch, J. D., & Prentice, R. L. (2002). The statistical analysis of failure time data, 2nd edition. New York, USA: Wiley.
Ko, T., Karayel, D., Boru, B., Ayhan, V., Cesur, ., & Parlak, A. (2014, January). Design and Implementation of the Control System of an Internal Combustion Engine Test Unit. Advances in Mechanical Engineering, 6, 914876. doi: 10.1155/2014/914876
Lasheras, F., Nieto, P., de Cos Juez, F., Bayn, R., & Surez, V. (2015, March). A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines. Sensors, 15(3), 7062–7083. doi: 10.3390/s150307062
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014, January). Prognostics and health management design for rotary machinery systemsReviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1-2), 314–334. doi: 10.1016/j.ymssp.2013.06.004
Li, R., Verhagen, W. J. C., & Curran, R. (2018). A Functional Architecture of Prognostics and Health Management using a Systems Engineering Approach. , 10.
Li, S., Zhang, G., & Wang, J. (2017, June). Civil aircraft health management research based on big data and deep learning technologies. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 154–159). Dallas, TX, USA: IEEE. doi: 10.1109/ICPHM.2017.7998321
Li, X., Ding, Q., & Sun, J.-Q. (2018, April). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1–11. doi: 10.1016/j.ress.2017.11.021
Lin, W.-C., & Ghoneim, Y. A. (2016, June). Model-based fault diagnosis and prognosis for Electric Power Steering systems. In 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1–8). Ottawa, ON, Canada: IEEE. doi: 10.1109/ICPHM.2016.7542840
Lipton, Z. C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. , 34.
Lipu, M. H., Hannan, M., Hussain, A., Hoque, M., Ker, P. J., Saad, M., & Ayob, A. (2018, December). A review of state of health and remaining useful life
estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. Journal of Cleaner Production, 205, 115–133. doi:
10.1016/j.jclepro.2018.09.065
Liu, J., Wang, W., Ma, F., Yang, Y., & Yang, C. (2012). A data-model-fusion prognostic framework for dynamic system state forecasting. Engineering Applications of Artificial Intelligence, 25(4), 814 - 823. (Special Section: Dependable System Modelling and Analysis) doi: https://doi.org10.1016j.engappai.2012.02.015
Liu, W., Tang, B., Han, J., Lu, X., Hu, N., & He, Z. (2015, April). The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 44, 466–472. doi: 10.1016/j.rser.2014.12.005
Luo, H., Huang, M., & Zhou, Z. (2018). Integration of multigaussian fitting and lstm neural networks for health monitoring of an automotive suspension component. Journal of Sound and Vibration, 428, 87 - 103. doi: https://doi.org/10.1016/j.jsv.2018.05.007
Luo, J., Namburu, M., Pattipati, K. R., Qiao, L., & Chigusa, S. (2010, March). Integrated Model-Based and Data-Driven Diagnosis of Automotive Antilock Braking Systems. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(2), 321–336. doi: 10.1109/TSMCA.2009.2034481
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, Sept). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38(5), 1156-1168. doi: 10.1109/TSMCA.2008.2001055
Ma, M. J., Lu, C., Zerhouni, M. N., & Cheng, Y. (2018). Aircraft Engine Health State Classification Using Stacked Denoising Autoencoder. , 6.
Matsuishi, M., & Endo, T. (1968). Fatigue of metals subjected to varying stress. Japan Soc. Mech. Engineering.
Meinhold, R. J., & Singpurwalla, N. D. (1983). Understanding the Kalman Filter.
Meneghetti, L., Terzi, M., Del Favero, S., Susto, G. A., & Cobelli, C. (2018). Data-Driven Anomaly Recognition for Unsupervised Model-Free Fault Detection in Artificial Pancreas. IEEE Transactions on Control Systems Technology, 1–15. doi: 10.1109/TCST.2018.2885963
Miner, M. (1945). Cumulative damage in fatigue. Journal of Applied Mechanics, 12, A159-A164.
Ming Yu, & Danwei Wang. (2014, July). Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types. IEEE Transactions on Industrial Electronics, 61(7), 3574–3586. doi: 10.1109/TIE.2013.2281159
Naderi, E., Meskin, N., & Khorasani, K. (2011). Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach. , 13.
Naderi, E., Meskin, N., & Khorasani, K. (2012). Nonlinear Fault Diagnosis of Jet Engines by Using a Multiple Model-Based Approach. Journal of Engineering for Gas Turbines and Power, 134(1), 011602. doi: 10.1115/1.4004152
Nguyen, D. V., Limmer, S., Yang, K., Olhofer, M., & B¨ack, T. (2019). Modelling and prediction of remaining useful lifetime for maintenance scheduling optimization of a car fleet. International Journal of Performability Engineering.
Ordez, C., Snchez Lasheras, F., Roca-Pardias, J., & Juez, F. J. d. C. (2019, January). A hybrid ARIMASVM model for the study of the remaining useful
life of aircraft engines. Journal of Computational and Applied Mathematics, 346, 184–191. doi: 10.1016/j.cam.2018.07.008
Paul, S., Kapoor, K., Jasani, D., Dudhwewala, R., Gowda, V. B., & Nair, T. R. G. (2008). Application of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul Solutions. , 7. Paul A. Gagniuc. (2017). Markov Chains: From Theory to Implementation and Experimentation. USA, NJ: John
Wiley & Sons.
Pecht, M.,&Jaai, R. (2010, March). A prognostics and health management roadmap for information and electronicsrich systems. Microelectronics Reliability, 50(3), 317–323. doi: 10.1016/j.microrel.2010.01.006
Pecht, M., & Jie Gu. (2009, June). Physics-of-failure-based prognostics for electronic products. Transactions of the Institute of Measurement and Control, 31(3-4), 309–322. doi: 10.1177/0142331208092031
Poritz, A. (1982). Linear predictive hidden Markov models and the speech signal. In ICASSP ’82. IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 7, pp. 1291–1294). Paris, France: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICASSP.1982.1171633
Rabiner, L. R. (1990). Readings in speech recognition. In A. Waibel & K.-F. Lee (Eds.), (pp. 267–296).
Radac, M., Precup, R., & Petriu, E. M. (2015, Nov). Modelfree primitive-based iterative learning control approach to trajectory tracking of mimo systems with experimental validation. IEEE Transactions on Neural Networks and Learning Systems, 26(11), 2925-2938. doi: 10.1109/TNNLS.2015.2460258
Radac, M.-B., & Precup, R.-E. (2017, January). Data-driven model-free slip control of anti-lock braking systems using reinforcement Q-learning. Neurocomputing, 275, 317–329. doi: 10.1016/j.neucom.2017.08.036
Radac, M.-B., Precup, R.-E., & Roman, R.-C. (2018, February). Data-driven model reference control of MIMO vertical tank systems with model-free VRFT
and Q-Learning. ISA Transactions, 73, 227–238. doi: 10.1016/j.isatra.2018.01.014
Ramasso, E. (2014). Investigating computational geometry for failure prognostics. , 18.
Ramasso, E., & Saxena, A. (2014). Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets. , 15.
Razavi-Far, R., Chakrabarti, S., Saif, M., & Zio, E. (2019, January). An integrated imputation-prediction scheme for prognostics of battery data with missing observations. Expert Systems with Applications, 115, 709–723. doi: 10.1016/j.eswa.2018.08.033
Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014, June). 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. doi: 10.1016/j.jpowsour.2014.01.085
Ross, T. J. (2010). Fuzzy logic with engineering applications (3rd ed ed.). Chichester, U.K: John Wiley. (OCLC: ocn430736639)
Saimurugan, M., Praveenkumar, T., Sabhrish, B., Sachin Menon, P., & Sanjiv, S. (2016, September). On-Road Testing of A Vehicle for Gearbox
Fault Detection using Vibration Signals. Indian Journal of Science and Technology, 9(34). doi: 10.17485/ijst/2016/v9i34/100957
Sankavaram, C., Kodali, A., Pattipati, K., Singh, S., Zhang, Y., & Salman, M. (2016). An Inference-based Prognostic Framework for Health Management of Automotive Systems. , 16.
Sankavaram, C., Pattipati, B., Kodali, A., Pattipati, K., Azam, M., Kumar, S., & Pecht, M. (2009, August). Model-based and data-driven prognosis of automotive and electronic systems. In 2009 IEEE International Conference on Automation Science and Engineering (pp. 96–101). Bangalore, India: IEEE. doi: 10.1109/COASE.2009.5234108
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008, October). Metrics for evaluating performance of prognostic techniques. In 2008 International Conference on Prognostics and Health Management (pp. 1–17). Denver, CO, USA: IEEE. doi: 10.1109/PHM.2008.4711436
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009, March). Evaluating algorithm performance metrics tailored for prognostics. In 2009 IEEE Aerospace conference (pp. 1–13). Big Sky, MT, USA: IEEE. doi: 10.1109/AERO.2009.4839666
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010, March). Evaluating prognostics performance for algorithms incorporating uncertainty estimates. In 2010 IEEE Aerospace Conference (pp. 1–11). Big Sky, MT: IEEE. doi: 10.1109/AERO.2010.5446828
Saxena, A., & Goebel, K. (2008). C-mapss data set. NASA Ames Prognostics Data Repository.
Selvi, D., Piga, D., & Bemporad, A. (2018, June). Towards direct data-driven model-free design of optimal controllers. In 2018 European Control Conference (ECC) (pp. 2836–2841). Limassol: IEEE. doi: 10.23919/ECC.2018.8550184
Shafi, U., Safi, A., Shahid, A. R., Ziauddin, S., & Saleem, M. Q. (2018). Vehicle Remote Health Monitoring and Prognostic Maintenance System. Journal
of Advanced Transportation, 2018, 1–10. doi: 10.1155/2018/8061514
Shao, Y., Liang, J., Gu, F., Chen, Z., & Ball, A. (2011, July). Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network. Journal of Physics: Conference Series, 305, 012063. doi: 10.1088/1742-6596/305/1/012063
Shih, T. I.-P., & Yang, V. (Eds.). (2014). Turbine aerodynamics, heat transfer, materials, and mechanics (No. 243). Reston, Va: American Inst. of Aeronautics and Astronautics, Inc. (OCLC: 903312698)
Shufen, Q., & Wanying, Z. (2013). Prognostic and Health Management System based on Flight Data. , 3.
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011, August). Remaining useful life estimation A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1–14. doi: 10.1016/j.ejor.2010.11.018
Sikorska, J., Hodkiewicz, M., & Ma, L. (2011a). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems
and Signal Processing, 25(5), 1803–1836. doi: 10.1016/j.ymssp.2010.11.018
Sikorska, J., Hodkiewicz, M., & Ma, L. (2011b, July). Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems
and Signal Processing, 25(5), 1803–1836. doi: 10.1016/j.ymssp.2010.11.018
Simon, D. (2008, June). A comparison of filtering approaches for aircraft engine health estimation. Aerospace Science and Technology, 12(4), 276–284.
doi: 10.1016/j.ast.2007.06.002
Singh, S., Kodali, A., & Pattipati, K. (2009, Aug). A factorial hidden markov model (fhmm)-based reasoner for diagnosing multiple intermittent faults.
In 2009 ieee international conference on automation science and engineering (p. 146-151). doi: 10.1109/COASE.2009.5234134
Sobczyk, K., & Spencer, B. (1993). Random fatigue: From data to theory. San Diego, USA: San Diego, CA: Academic.
Spall, J. C., & Cristion, J. A. (1998, Sep.). Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Transactions on Automatic Control, 43(9), 1198-1210. doi: 10.1109/9.718605
Stainier, L., Leygue, A., & Ortiz, M. (2019, August). Model-Free Data-Driven Methods in Mechanics: Material Data Identification and Solvers. Computational Mechanics, 64(2), 381–393. (arXiv: 1903.07983) doi: 10.1007/s00466-019-01731-1
Su, S., Zhang, W., & Zhao, S. (2014). Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR. Mathematical Problems in Engineering, 2014, 1–9. doi: 10.1155/2014/692848
Sutharssan, T., Stoyanov, S., Bailey, C., & Yin, C. (2015, July). Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms. The Journal of Engineering, 2015(7), 215–222. doi: 10.1049/joe.2014.0303
Tahan, M., Tsoutsanis, E., Muhammad, M., & Abdul Karim, Z. (2017, July). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, 198, 122–144. doi: 10.1016/j.apenergy.2017.04.048
Taie, M. A., Diab, M., & ElHelw, M. (2012, October). Remote prognosis, diagnosis and maintenance for automotive architecture based on least
squares support vector machine and multiple classifiers. In 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems (pp. 128–134). St. Petersburg, Russia: IEEE. doi:10.1109/ICUMT.2012.6459652
Tan, K. K., Lee, T. H., Huang, S. N., & Leu, F. M. (2001, Apr 01). Adaptive-predictive control of a class of siso nonlinear systems. Dynamics and Control, 11(2), 151–174. doi: 10.1023/A:1012583811904
Tang, L., Kacprzynski, G. J., Goebel, K., Saxena, A., Saha, B., & Vachtsevanos, G. (2008, Oct). Prognosticsenhanced automated contingency management for advanced autonomous systems. In 2008 international conference on prognostics and health management (p. 1-9). doi: 10.1109/PHM.2008.4711448
Tian, J., Xiong, R., & Yu, Q. (2019, February). Fractional-Order Model-Based Incremental Capacity Analysis for Degradation State Recognition of Lithium-Ion Batteries. IEEE Transactions on Industrial Electronics, 66(2), 1576–1584. doi: 10.1109/TIE.2018.2798606
Tinga, T. (2013). Springer Series in Reliability Engineering. London, Heidelberg, New York, Dordrecht: Springer.
Tipping, M. E. (1999). The Relevance Vector Machine. , 7.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and Health Management: A Review on Data Driven Approaches. Mathematical Problems in Engineering, 2015, 1–17. doi: 10.1155/2015/793161
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems: Vachtsevanos/Intelligent Fault Diagnosis. Hoboken, NJ, USA: John Wiley & Sons, Inc. doi: 10.1002/9780470117842
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York, NY: Springer New York. (OCLC: 905485685)
Vatani, A., Khorasani, K., & Meskin, N. (2015, June). Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks. In Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy; Honors and Awards (p. V006T05A030). Montreal, Quebec, Canada: ASME. doi: 10.1115/GT2015-44101
Vianna, W. O. L., Rodrigues, L. R., & Yoneyama, T. (2015). Aircraft Line Maintenance Planning Based on PHM Data and Resources Availability Using Large Neighborhood Search. , 7.
Vogl, G. W., Weiss, B. A., & Donmez, M. A. (2014). Standards for Prognostics and Health Management (PHM) Techniques within Manufacturing Operations. , 13.
Vogl, G. W., Weiss, B. A., & Helu, M. (2016, Jun 09). A review of diagnostic and prognostic capabilities and best practices for manufacturing. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-016-1228-8
Wang, D., Miao, Q., & Pecht, M. (2013). Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. Journal of Power Sources, 239, 253 - 264. doi: https://doi.org/10.1016/j.jpowsour.2013.03.129
Wang, J., Mao, X., Zhu, K., Song, J., & Zhuo, B. (2009, September). An intelligent diagnostic tool for electronically controlled diesel engine. Mechatronics, 19(6), 859–867. doi: 10.1016/j.mechatronics.2009.04.009
Wang, M.-H., Chao, K.-H., Sung, W.-T., & Huang, G.-J. (2010, April). Using ENN-1 for fault recognition of automotive engine. Expert Systems with Applications, 37(4), 2943–2947. doi: 10.1016/j.eswa.2009.09.041
Wang, T., Jianbo Yu, Siegel, D., & Lee, J. (2008, October). A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems. In 2008 International Conference on Prognostics and Health Management (pp. 1–6). Denver, CO, USA: IEEE. doi: 10.1109/PHM.2008.4711421
Wang, Y., Limmer, S., Olhofer, M., Emmerich, M. T. M., & B¨ack, T. (2019, June). Vehicle fleet maintenance scheduling optimization by multi-objective
evolutionary algorithms. In 2019 ieee congress on evolutionary computation (cec) (p. 442-449). doi: 10.1109/CEC.2019.8790142
Wheeler, K. R., Kurtoglu, T., & Poll, S. D. (2009). A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics. In Volume 2: 29th Computers and Information in Engineering Conference, Parts A and B (pp. 1287–1298). San Diego, California, USA: ASME. doi: 10.1115/DETC2009-87073
Wu, J. (2017). Introduction to Convolutional Neural Networks. , 31.
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018). Recent advances in prognostics and health management for advanced manufacturing
paradigms. Reliability Engineering and System Safety, 178(C), 255-268. Retrieved from https://ideas.repec.org/a/eee/reensy/v178y2018icp255-doi: 10.1016/j.ress.2018.06.02
Xing, Y., Ma, E. W., Tsui, K.-L., & Pecht, M. (2013). An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 53(6), 811 - 820. doi: https://doi.org/10.1016/j.microrel.2012.12.003
Xu, J., Wang, Y., & Xu, L. (2014, April). PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data. IEEE Sensors Journal, 14(4), 1124–1132. doi: 10.1109/JSEN.2013.2293517
Xu, J., & Xu, L. (2011, June). Health management based on fusion prognostics for avionics systems. Journal of Systems Engineering and Electronics, 22(3), 428–436. doi: 10.3969/j.issn.1004-4132.2011.03.010
Yang, C., Song, P., & Liu, X. (2017, Apr 18). Failure prognostics of heavy vehicle hydro-pneumatic spring based on novel degradation feature and support vector regression. Neural Computing and Applications. doi: 10.1007/s00521-017-2986-8
Yang, F., Xing, Y., Wang, D., & Tsui, K.-L. (2016, February). A comparative study of three model-based algorithms for estimating state-of-charge of lithiumion batteries under a new combined dynamic loading profile. Applied Energy, 164, 387–399. doi: 10.1016/j.apenergy.2015.11.072
Yang, L., Wang, J., & Zhang, G. (2016, June). Aviation PHM system research framework based on PHM big data center. In 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1–5). Ottawa, ON, Canada: IEEE. doi: 10.1109/ICPHM.2016.7542824
Ye, M., Guo, H., & Cao, B. (2017, March). A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter. Applied Energy, 190, 740–748. doi: 10.1016/j.apenergy.2016.12.133
You, G.-w., Park, S., & Oh, D. (2016, August). Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach. Applied Energy, 176, 92–103. doi: 10.1016/j.apenergy.2016.05.051
Zhang, J., & Lee, J. (2011, August). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196(15), 6007–6014. doi:
10.1016/j.jpowsour.2011.03.101
Zhang, M. Y., Liu, J., Hanachi, D. H., Yu, M. X., & Yang, M. Y.-B. (2018). Physics-based Model and Neural Network Model for Monitoring Starter Degradation of APU. , 7.
Zhang, X., Kang, J., Zhao, J. S., & Cao, D. C. (2013, July). Features for fault diagnosis and prognosis of gearbox. Chemical Engineering Transactions, 1027–1032. doi: 10.3303/CET1333172
Zhao, Z., Liang, B., Wang, X., & Lu, W. (2017). Remaining useful life prediction of aircraft engine based on degradation pattern learning. Reliability Engineering and System Safety, 164, 74 - 83. doi: https://doi.org/10.1016/j.ress.2017.02.007
Zheng, X., & Fang, H. (2015). An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful
life and short-term capacity prediction. Reliability Engineering & System Safety, 144, 74 - 82. doi: https://doi.org/10.1016/j.ress.2015.07.013
Zhong, S., Li, M. Z., Lin, M. L., & Zhang, D. Y. (2018). Aero-Engine Exhaust Gas Temperature Prognostic Model Based on Gated Recurrent Unit Network.
, 5.
Zou, K.-X., Ma, H.-D., Fang, H.-Z., & Yi, D.-W. (2011, May). Study of prognostics for spacecraft based-on particle swarm optimized neural network. In 2011 Prognostics and System Health Managment Confernece (pp. 1–5). Shenzhen, China: IEEE. doi: 10.1109/PHM.2011.5939479
Section
Technical Papers