Computational algorithm; Design and implementations; Different stages; Dynamic regimes; Health management; Remaining useful lives; Computer Science Applications; Applied Mathematics
Abstract :
[en] Complex systems are expected to play a key role in the progress of Prognostics Health Management but the breadth of technologies that will highlight gaps in the dynamic regimes are expected to become more prominent and likely more challenging in the future. The design and implementation of sophisticated computational algorithms have become a critical aspect to solve problems in many prognostic applications for multiple regimes. In addition to a wide variety of conventional computational and cognitive paradigms such as machine learning and data mining fields, specific applications in prognostics have led to a wealth of newly proposed methods and techniques. This paper reviews practices for modeling prognostics and remaining useful life applications in complex systems working under multiple operational regimes. An analysis is provided to compare and combine the findings of previously published studies in the literature, and it assesses the effectiveness of techniques for different stages of prognostic development. The paper concludes with some speculations on the likely advances in fusion of advanced methods for case specific modeling.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
BEKTAS, Oguz ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal ; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom ; Ministry of National Education, Ankara, Turkey
Marshall, Jane; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
Jones, Jeffrey A.; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom
External co-authors :
no
Language :
English
Title :
Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives
Jamshidi M (2008) Systems of systems engineering: principles and applications. CRC Press, Boca Raton
Günel A, Meshram A, Bley T, Schuetze A, Klusch M (2013) Statistical and semantic multisensor data evaluation for fluid condition monitoring in wind turbines. In: Proceedings of 16th international conference on sensors and measurement technology, Germany
Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: International conference on prognostics and health management, 2008. PHM 2008. IEEE, pp 1–9
Uckun S, Goebel K, Lucas PJ (2008) Standardizing research methods for prognostics. In: International conference on prognostics and health management, 2008. PHM 2008. IEEE, pp 1–10
Cempel C (2009) Generalized singular value decomposition in multidimensional condition monitoring of machines—a proposal of comparative diagnostics. Mech Syst Signal Process 23(3):701
Wang T (2010) Trajectory similarity based prediction for remaining useful life estimation. Ph.D. thesis, University of Cincinnati
Bektas O (2018) An adaptive data filtering model for remaining useful life estimation. Ph.D. thesis, The University of Warwick
Pecht M (2008) Prognostics and health management of electronics. Wiley, New York
Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50(1–4):297
Jones J, Warrington L, Davis N (2001) The use of a discrete event simulation to model the achievement of maintenance free operating time for aerospace systems. In: Reliability and maintainability symposium, 2001. Proceedings. Annual. IEEE, pp 170–175
Medjaher K, Zerhouni N, Baklouti J (2013) Data-driven prognostics based on health indicator construction: Application to pronostia’s data. In: 2013 European control conference (ECC). IEEE, pp 1451–1456
ISO-13381, ISO 13381-1:2015 condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. Tech. rep., International Organization for Standardization (2015)
Efthymiou K, Papakostas N, Mourtzis D, Chryssolouris G (2012) On a predictive maintenance platform for production systems. Proc CIRP 3:221
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based. Mech Syst Signal Process 20(7):1483
Lewis K (2017) Technical note– TN 016: 2017 maintenance requirements analysis manual. Technical report, the Asset Standards Authority (ASA), Australia
Goode K, Moore J, Roylance B (2000) Plant machinery working life prediction method utilizing reliability and condition-monitoring data. Proc Inst Mech Eng Part E J Process Mech Eng 214(2):109
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. Mech Syst Signal Process 42(1):314
Tobon-Mejia DA, Medjaher K, Zerhouni N (2010) The ISO 13381-1 standard’s failure prognostics process through an example. In: Prognostics and health management conference, 2010. PHM’10. IEEE, pp 1–12
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483
Bektas O, Jones JA, Sankararaman S, Roychoudhury I, Goebel K (2018) Reconstructing secondary test database from PHM08 challenge data set. Data Brief 21:2464
Bar-Yam Y (2003) Complexity of military conflict: multiscale complex systems analysis of littoral warfare, Report to Chief of Naval Operations Strategic Studies Group
Vassiliadis P (1998) Modeling multidimensional databases, cubes and cube operations. In: Tenth international conference on scientific and statistical database management, 1998. Proceedings. IEEE, pp 53–62
Cempel C (2003) Multidimensional condition monitoring of mechanical systems in operation. Mech Syst Signal Process 17(6):1291
Li Y, Kurfess T, Liang S (2000) Stochastic prognostics for rolling element bearings. Mech Syst Signal Process 14(5):747
Menezes JMP, Barreto GA (2008) Long-term time series prediction with the narx network: an empirical evaluation. Neurocomputing 71(16):3335
Lam J, Sankararaman S, Stewart B (2014) Enhanced trajectory based similarity prediction with uncertainty quantification. In: Annual conference of the prognostics and health management society 2014
Cattin DP (2013) Image restoration: introduction to signal and image processing. MIAC Univ Basel Retr 11:93
Zaidan MA (2014) Bayesian approaches for complex system prognostics. Ph.D. thesis, University of Sheffield
Eker ÖF (2015) A hybrid prognostic methodology and its application to well-controlled engineering systems. Ph.D. thesis, Cranfield University
Paris PC, Erdogan F (1963) A critical analysis of crack propagation laws. ASME
Li Y, Billington S, Zhang C, Kurfess T, Danyluk S, Liang S (1999) Adaptive prognostics for rolling element bearing condition. Mech Syst Signal Process 13(1):103
Li CJ, Choi S (2002) Spur gear root fatigue crack prognosis via crack diagnosis and fracture mechanics. In: Proceedings of the 56th meeting of the society of mechanical failures prevention technology, pp 311–320
Li CJ, Lee H (2005) Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mech Syst Signal Process 19(4):836
Oppenheimer CH, Loparo KA (2002) Physically based diagnosis and prognosis of cracked rotor shafts. In: AeroSense 2002. International Society for Optics and Photonics, pp 122–132
Orsagh RF, Sheldon J, Klenke CJ (2003) Prognostics/diagnostics for gas turbine engine bearings. In: ASME turbo expo 2003, collocated with the 2003 international joint power generation conference. American Society of Mechanical Engineers, pp 159–167
Orsagh R, Roemer M, Sheldon J, Klenke CJ (2004) A comprehensive prognostics approach for predicting gas turbine engine bearing life. In: ASME turbo expo 2004: power for land, sea, and air. American Society of Mechanical Engineers, pp 777–785
Kacprzynski G, Sarlashkar A, Roemer M, Hess A, Hardman B (2004) Predicting remaining life by fusing the physics of failure modeling with diagnostics. JOM J Miner Met Mater Soc 56(3):29
Marble S, Morton BP (2006) Predicting the remaining life of propulsion system bearings. In: Aerospace conference, 2006. IEEE, p 8
Heng A, Zhang S, Tan AC, Mathew J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process 23(3):724
Brotherton BP, Jahns G, Jacobs J, Wroblewski D (2000) Prognosis of faults in gas turbine engines. In: Aerospace conference proceedings 2000, vol 6. IEEE, 163–171
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 Trans Reliab 63(1):191
Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803
George V, Frank L, Michael R, Andrew H, Biqing W (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, New York
Butler KL (1996) An expert system based framework for an incipient failure detection and predictive maintenance system. In: International conference on intelligent systems applications to power systems. Proceedings, ISAP’96. IEEE, pp 321–326
Biagetti T, Sciubba E (2004) Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems. Energy 29(12–15):2553
Feng E, Yang H, Rao M (1998) Fuzzy expert system for real-time process condition monitoring and incident prevention. Expert Syst Appl 15(3–4):383
Satish B, Sarma N (2005) A fuzzy bp approach for diagnosis and prognosis of bearing faults in induction motors. In: Power engineering society general meeting, 2005. IEEE, pp 2291–2294
Dmitry K, Dmitry V (2004) An algorithm for rule generation in fuzzy expert systems. In: Proceedings of the 17th international conference on pattern recognition. ICPR 2004, vol 1. IEEE, pp 212–215
Eker ÖF, Camci F, Jennions IK (2014) A similarity-based prognostics approach for remaining useful life prediction. In: European conference of prognostics and health management society 2014
Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27(5):1037
Wang T, Yu J, Siegel D, Lee J (2008) A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: International conference on prognostics and health management. PHM 2008. IEEE, pp 1–6
Ramasso E (2014) Investigating computational geometry for failure prognostics. Int J Progn Health Manag 5(1):005
Bektas O, Alfudail A, Jones JA (2017) Reducing dimensionality of multi-regime data for failure prognostics. J Fail Anal Prev 17(6):1268
An D, Choi JH, Kim NH (2013) Prognostics 101: a tutorial for particle filter-based prognostics algorithm using matlab. Reliab Eng Syst Saf 115:161
Bayes T, Price R, Canton J (1763) An essay towards solving a problem in the doctrine of chances. C. Davis, Printer to the Royal Society of London
Orchard ME, Vachtsevanos GJ (2009) A particle-filtering approach for on-line fault diagnosis and failure prognosis. Trans Inst Meas Control 31:221–246
Orchard M, Wu B, Vachtsevanos G (2005) A particle filtering framework for failure prognosis. In: World tribology congress III. American Society of Mechanical Engineers, pp 883–884
Saha B, Goebel K (2009) Modeling li-ion battery capacity depletion in a particle filtering framework. In: Proceedings of the annual conference of the prognostics and health management society, pp 2909–2924
Miao Q, Xie L, Cui H, Liang W, Pecht M (2013) Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab 53(6):805
Wang P, Gao RX (2014) Particle filtering-based system degradation prediction applied to jet engines. In: Annual conference of the prognostics and health management society. Citeseer, p 1
Hu C, Youn BD, Chung J (2012) A multiscale framework with extended kalman filter for lithium-ion battery soc and capacity estimation. Appl Energy 92:694
Julier SJ, Uhlmann JK (1997) New extension of the kalman filter to nonlinear systems. In: AeroSense 97. International society for optics and photonics, pp 182–193
Swanson DC (2001) A general prognostic tracking algorithm for predictive maintenance. In: Aerospace conference, 2001, IEEE Proceedings, vol 6. IEEE, pp 2971–2977
Bunks C, McCarthy D, Al-Ani T (2000) Condition-based maintenance of machines using hidden Markov models. Mech Syst Signal Process 14(4):597
Camci F (2005) Process monitoring, diagnostics and prognostics using support vector machines and hidden markov models. Ph.D. thesis, Wayne State University, Detroit
Baruah P, Chinnam RB (2005) Hmms for diagnostics and prognostics in machining processes. Int J Prod Res 43(6):1275
Dong M, He D (2007) A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech Syst Signal Process 21(5):2248
Ramasso E (2009) Contribution of belief functions to hidden markov models with an application to fault diagnosis. In: IEEE international workshop on machine learning for signal processing. MLSP 2009. IEEE, pp 1–6
Orhan E (2012) Particle filtering. Center for Neural Science, University of Rochester, Rochester, NY 8(11)
Ghahramani Z (2001) An introduction to hidden Markov models and bayesian networks. Int J Pattern Recognit Artif Intell 15(01):9
Batko W (1984) Prediction method in technical diagnostics, Unpublished doctoral dissertation, Cracov Mining Academy
Kazmierczak K (1983) Application of autoregressive prognostic techniques in diagnostics. In: Proceedings of the vehicle diagnostics conference. Tuczno, Poland
Cempel C (1987) Simple condition forecasting techniques in vibroacoustical diagnostics. Mech Syst Signal Process 1(1):75
Box GE, Jenkins GM, Reinsel GC (2015) Time series analysis: forecasting and control. Wiley, New York
Whitle P (1951) Hypothesis testing in time series analysis, vol 4. Almqvist & Wiksells, Uppsala
Ramasso E (2014) Investigating computational geometry for failure prognostics in presence of imprecise health indicator: Results and comparisons on c-mapss datasets. In: European conference on prognostics and health management 5(1): 005
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
Chatterjee S, Hadi AS (1986) Influential observations, high leverage points, and outliers in linear regression. Stat Sci 1:379–393
Freedman DA (2009) Statistical models: theory and practice. Cambridge University Press, Cambridge
Holland SM (2008) Principal components analysis (PCA). Department of Geology, University of Georgia, Athens, GA, pp 30, 602–2501
Peel L (2008) Data driven prognostics using a kalman filter ensemble of neural network models. In: International conference on prognostics and health management, 2008. PHM 2008. IEEE, pp 1–6
Malinowski S, Chebel-Morello B, Zerhouni N (2015) Remaining useful life estimation based on discriminating shapelet extraction. Reliab Eng Syst Saf 142:279
Rigamonti M, Baraldi P, Zio E, Roychoudhury I, Goebel K, Poll S (2016) Echo state network for the remaining useful life prediction of a turbofan engine. In: Proceedings of the third European prognostic and health management conference, PHME
Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for CMAPSS datasets. Int J Progn Health Manag 5(2):1
Goebel K, Saha B, Saxena A, Mct N, Riacs N (2008) A comparison of three data-driven techniques for prognostics. In: 62nd meeting of the society for machinery failure prevention technology (MFPT), pp 119–131
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Kozlowski JD, Watson MJ, Byington CS, Garga AK, Hay TA (1999, 2001) Electrochemical cell diagnostics using online impedance measurement, state estimation and data fusion techniques. In: Intersociety energy conversion engineering conference, vol 2. SAE, pp 981–986
Byington CS, Watson M, Edwards D (2004) Data-driven neural network methodology to remaining life predictions for aircraft actuator components. In: Aerospace conference, 2004. Proceedings. IEEE, vol 6., pp 3581–3589
Krenker A, Bester J, Kos A (2011) Introduction to the artificial neural networks. Artificial neural networks: methodological advances and biomedical applications. InTech, Rijeka. ISBN, pp 978–953
Barad SG, Ramaiah PV et al (2012) Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine. Mech Syst Signal Process 27:729
Murata N, Yoshizawa S, Amari Si (1994) Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans Neural Netw 5(6):865
Heimes FO (2008) Recurrent neural networks for remaining useful life estimation. In: International conference on prognostics and health management, 2008. PHM 2008. IEEE, pp 1–6
Peng Y, Wang H, Wang J, Liu D, Peng X (2012) A modified echo state network based remaining useful life estimation approach. In: 2012 IEEE conference on prognostics and health management (PHM), pp 1–7
Abbas M (2010) System-level health assessment of complex engineered processes. Ph.D. thesis, Georgia Institute of Technology
Jianzhong S, Hongfu Z, Haibin Y, Pecht M (2010) Study of ensemble learning-based fusion prognostics. In: Prognostics and health management conference, 2010. PHM’10. IEEE, pp 1–7
Riad A, Elminir H, Elattar H (2010) Evaluation of neural networks in the subject of prognostics as compared to linear regression model. Int J Eng Technol 10(1):52
Javed K, Gouriveau R, Zemouri R, Zerhouni N (2012) Features selection procedure for prognostics: an approach based on predictability. IFAC Proc Vol 45(20):25
Bektas O, Jones J (2016) NARX time series model for remaining useful life estimation of gas turbine engines In: Proceedings of Third European Conference of the Prognostics and Health Management Society
Parker Jr BE, Nigro TM, Carley MP, Barron RL, Ward DG, Poor HV, Rock D, DuBois TA (1993) Helicopter gearbox diagnostics and prognostics using vibration signature analysis. In: Optical engineering and photonics in aerospace sensing. International society for optics and photonics, pp 531–542
Bonissone PP, Goebel K (2002) When will it break? a hybrid soft computing model to predict time-to-break margins in paper machines. In: International symposium on optical science and technology. International society for optics and photonics, pp 53–64
Baraldi P, Compare M, Sauco S, Zio E (2013) Ensemble neural network-based particle filtering for prognostics. Mech Syst Signal Process 41(1):288
Haykin S, Li XB (1995) Detection of signals in chaos. Proc IEEE 83(1):95
Haykin S, Principe J (1998) Making sense of a complex world [chaotic events modeling]. IEEE Signal Process Mag 15(3):66
Demuth H, Beale M, Hagan M (2008) Neural network toolbox TM 6, User’s guide, pp 37–55
Principe JC, Euliano NR, Lefebvre WC (1999) Neural and adaptive systems: fundamentals through simulations with CD-ROM. Wiley, New York
Bektas O, Jones JA, Sankararaman S, Roychoudhury I, Goebel K (2019) A neural network filtering approach for similarity-based remaining useful life estimation. Int J Adv Manuf Technol 101(1–4):87–103
Sun B, Zeng S, Kang R, Pecht M (2010) Benefits analysis of prognostics in systems. In: Prognostics and health management conference, 2010. PHM’10. IEEE, pp 1–8
Ho YC, Pepyne DL (2001) Simple explanation of the no free lunch theorem of optimization. In: Proceedings of the 40th IEEE conference on decision and control, 2001, vol 5. IEEE, pp 4409–4414
Koppen M (2004) No-free-lunch theorems and the diversity of algorithms. In: Congress on evolutionary computation, 2004. CEC2004, vol 1. IEEE, pp 235–241
Coble JB (2010) Merging data sources to predict remaining useful life—an automated method to identify prognostic parameters. Ph.D. thesis, University of Tennessee
