Abstract
The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics.
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Goebel K, Saha B, Saxena A (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
Randall RB (2011) Vibration-based condition monitoring: industrial, aerospace and automotive applications. Wiley
Schwabacher M, Goebel K (2007) A survey of artificial intelligence for prognostics. In: Aaai fall symposium, pp 107–114
Brown D, Kalgren P, Roemer M (2007) Electronic prognostics-a case study using switched-mode power supplies (smps). IEEE Instrum Measur Mag 10(4):20–26
Bektas O, Alfudail A, Jones JA (2017) Reducing dimensionality of multi-regime data for failure prognostics. J Fail Anal Prev 17(6):1268
Si XS, Wang W, Hu CH, Chen MY, Zhou DH (2013) A wiener-process-based degradation model with a recursive filter algorithm for remaining useful life estimation. Mech Syst Signal Process 35(1):219–237
Chinnam RB, Mohan P (2002) Online reliability estimation of physical systems using neural networks and wavelets. Int J Smart Eng Sys Des 4(4):253–264
Sheldon J, Lee H, Watson M, Byington C, Carney E (2007) Detection of incipient bearing faults in a gas turbine engine using integrated signal processing techniques. In: Annual forum proceedings-American helicopter society, vol 63. American Helicopter Society, Inc, p 925
Camci F (2005) Process monitoring, diagnostics and prognostics using support vector machines and hidden Markov models. Ph.D. thesis. Wayne State University, Detroit
Liu Q, Dong M, Peng Y (2012) A novel method for online health prognosis of equipment based on hidden semi-Markov model using sequential monte carlo methods. Mech Syst Signal Process 32:331–348
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. 2004 IEEE, vol 6. IEEE, pp 3581–3589
Byington CS, Watson M, Edwards D (2004) Dynamic signal analysis and neural network modeling for life prediction of flight control actuators. In: Proceedings of the American helicopter society 60th annual forum
Saha B, Poll S, Goebel K, Christophersen J (2007) An integrated approach to battery health monitoring using bayesian regression and state estimation. In: Autotestcon, 2007 IEEE. IEEE, pp 646–653
Pola DA, Navarrete HF, Orchard ME, Rabié RS, Cerda MA, Olivares BE, Silva JF, Espinoza PA, Pérez A (2015) Particle-filtering-based discharge time prognosis for lithium-ion batteries with a statistical characterization of use profiles. IEEE Trans Reliab 64(2):710–720
Hong S, Zhou Z (2012) Application of gaussian process regression for bearing degradation assessment. In: 2012 6th International conference on new trends in information science and service science and data mining (ISSDM). IEEE, pp 644–648
Chiachío J, Chiachío M, Sankararaman S, Saxena A, Goebel K (2015) Condition-based prediction of time-dependent reliability in composites. Reliab Eng Syst Safety 142:134–147
Medjaher K, Zerhouni N (2013) Hybrid prognostic method applied to mechatronic systems. Int J Adv Manuf Technol 69(1-4):823–834
Perez A, Moreno R, Moreira R, Orchard M, Strbac G (2016) Effect of battery degradation on multi-service portfolios of energy storage. IEEE Trans Sustain Energy 7(4):1718–1729
Pérez A, Quintero V, Rozas H, Jaramillo F, Moreno R, Orchard M (2017) Modelling the degradation process of lithium-ion batteries when operating at erratic state-of-charge swing ranges. In: International conference on control, decision and information technologies
Pérez A, Quintero V, Rozas H, Jimenez D, Jaramillo F, Orchard M (2017) Lithium-ion battery pack arrays for lifespan enhancement. In: 2017 CHILEAN Conference on electrical, electronics engineering, information and communication technologies (CHILECON). IEEE, pp 1–5
Jouin M, Gouriveau R, Hissel D, Péra MC, Zerhouni N (2016) Degradations analysis and aging modeling for health assessment and prognostics of pemfc. Reliab Eng Syst Safety 148:78–95
Pastor-Fernández C, Widanage WD, Chouchelamane G, Marco J (2016) A soh diagnosis and prognosis method to identify and quantify degradation modes in li-ion batteries using the ic/dv technique. In: IET Conference publications (CP691), pp 1–6
Saha B, Celaya JR, Wysocki PF, Goebel KF (2009) Towards prognostics for electronics components. In: Aerospace conference, 2009 IEEE. IEEE, pp 1–7
Wang T (2010) Trajectory similarity based prediction for remaining useful life estimation. University of Cincinnati
Zaidan MA, Mills AR, Harrison RF (2013) Bayesian framework for aerospace gas turbine engine prognostics. In: Aerospace conference, 2013 IEEE. IEEE, pp 1–8
Sikorska J, Hodkiewicz M, Ma L (2011) Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process 25(5):1803–1836
Kothamasu R, Huang SH, VerDuin WH (2006) System health monitoring and prognostics—a review of current paradigms and practices. Int J Adv Manuf Technol 28(9–10):1012–1024
Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. International Journal of Advanced Manufacturing Technology 50(1–4):297–313
Niknam SA, Kobza J, Hines JW (2017) Techniques of trend analysis in degradation-based prognostics. Int J Adv Manuf Technol 88(9–12):2429–2441
Xiao Q, Fang Y, Liu Q, Zhou S (2018) Online machine health prognostics based on modified duration-dependent hidden semi-Markov model and high-order particle filtering. Int J Adv Manuf Technol 94 (1-4):1283–1297
Zhou Y, Xue W (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol, 1–15
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
Mahamad AK, Saon S, Hiyama T (2010) Predicting remaining useful life of rotating machinery based artificial neural network. Comput Math Appl 60(4):1078–1087
Tian Z (2012) An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. J Intell Manuf 23(2):227–237
Bonissone PP, Goebel K (2001) Soft computing applications in equipment maintenance and service. In: IFSA World Congress and 20th NAFIPS international conference, 2001. Joint 9th. IEEE, pp 2752–2757
Yan J, Liu Y, Han S, Qiu M (2013) Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine. Renew Sustain Energy Rev 27:613–621
Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223
Widodo A, Yang BS (2011) Machine health prognostics using survival probability and support vector machine. Expert Syst Appl 38(7):8430–8437
Saxena A, Wu B, Vachtsevanos G (2005) Integrated diagnosis and prognosis architecture for fleet vehicles using dynamic case-based reasoning. In: Autotestcon, 2005. IEEE, pp 96–102
Byington CS, Watson M, Edwards D, Dunkin B (2003) In-line health monitoring system for hydraulic pumps and motors. In: Aerospace conference, 2003. Proceedings. 2003 IEEE, vol 7. IEEE, pp 3279–3287
Watson M, Byington C, Edwards D, Amin S (2005) Dynamic modeling and wear-based remaining useful life prediction of high power clutch systemsⒸ. Tribol Lubric Technol 61(12):38
Saha B, Goebel K, Poll S, Christophersen J (2009) Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Trans Instrum Measur 58(2):291–296
Gebraeel N (2006) Sensory-updated residual life distributions for components with exponential degradation patterns. IEEE Trans Autom Sci Eng 3(4):382–393
Amin S, Byington C, Watson M (2005) Fuzzy inference and fusion for health state diagnosis of hydraulic pumps and motors. In: Annual Meeting of the North American fuzzy information processing society, 2005. NAFIPS 2005. IEEE, pp 13–18
Volponi A (2005) Data fusion for enhanced aircraft engine prognostics and health management. Citeseer
Bar-Yam Y (2003) Complexity of military conflict: multiscale complex systems analysis of littoral warfare. Report to Chief of Naval Operations Strategic Studies Group
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: Proc. 16th Intl. conf. 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):701–711
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
Tumer IY, Huff EM (2003) Analysis of triaxial vibration data for health monitoring of helicopter gearboxes. J Vibr Acoust 125(1):120–128
Suo H, Wan J, Zou C, Liu J (2012) Security in the internet of things a review. In: 2012 international conference on computer science and electronics engineering (ICCSEE), vol 3. IEEE, pp 648–651
Kan MS, Tan AC, Mathew J (2015) A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech Syst Signal Process 62:1–20
Saxena A, Goebel K (2008) Phm08 challenge data set. NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center. Moffett Field
Boussif A (2016) Contributions to model-based diagnosis of discrete-event systems. Ph.D. thesis Université de Lille1-Sciences et Technologies
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
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, 2008. PHM 2008. IEEE, pp 1–6
Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for cmapss datasets. Int J Prognostics Health Manag 5(2):1–15
Arbib MA (2003) The handbook of brain theory and neural networks. MIT Press
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):865–872
Simões JM, Gomes CF, Yasin MM (2011) A literature review of maintenance performance measurement: a conceptual framework and directions for future research. J Qual Maint Eng 17(2):116–137
Bask A, Spens K, Uusipaavalniemi S, Juga J (2008) Information integration in maintenance services. Int J Product Perform Manag 58(1):92–110
Frederick D, DeCastro J, Litt J (2007) User’s guide for the commercial modular aero-propulsion system simulation (c-mapss) (tech. rep.). Cleveland, p 44135
Lam J, Sankararaman S, Stewart B (2014) Enhanced trajectory based similarity prediction with uncertainty quantification. PHM 2014
McLachlan G, Peel D (2004) Finite mixture models. Wiley
Ramasso E (2014) Investigating computational geometry for failure prognostics. Int J Prognostics Health Manag 5(1):005
Sugeno M, Yasukawa T (1993) A fuzzy-logic-based approach to qualitative modeling. IEEE Trans Fuzzy Syst 1(1):7–31
Ultsch A (1993) Self-organizing neural networks for visualisation and classification. In: Information and classification. Springer, pp 307–313
Coble JB (2010) Merging data sources to predict remaining useful life–an automated method to identify prognostic parameters. University of Tennessee
Barad SG, Ramaiah P, Giridhar R, Krishnaiah G (2012) Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine. Mech Syst Signal Process 27:729–742
Krenker A, Kos A, Bešter J (2011) Introduction to the artificial neural networks. INTECH Open Access Publisher
Heath G (2012) Declare net of neural network in matlab. [Online forum comment, Last Accessed 30 Nov 2017] https://www.mathworks.com/matlabcentral/profile/authors/2929937-greg-heath
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958
Lawrence S, Giles CL, Tsoi AC (1997) Lessons in neural network training: overfitting may be harder than expected. In: AAAI/IAAI. Citeseer, pp 540–545
MacKay DJ (1992) A practical Bayesian framework for backpropagation networks. Neural computation 4(3):448–472
Foresee FD, Hagan MT (1997) Gauss-newton approximation to Bayesian learning. In: International conference on neural networks, 1997, vol 3. IEEE, pp 1930–1935
Demuth H, Beale M, Hagan M (2015) Matlab: neural network toolbox: user’s guide matlab r2015b. The MathWorks 2009
Demuth H, Beale M, Hagan M (2008) Neural network toolbox™ 6. User’s guide, 37–55
Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688
Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1(2):111–153
Saxena A, Celaya J, Balaban E, Goebel K, Saha B, Saha S, Schwabacher M (2008) Metrics for evaluating performance of prognostic techniques. In: International conference on prognostics and health management, 2008. phm 2008. IEEE, pp 1–17
Goebel K, Saxena A, Saha S, Saha B, Celaya J (2011) Machine learning and knowledge discovery for engineering systems health management, chap. Prognostic performance metrics. CRC Press, pp 148–174
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The material is based upon work supported in part by NASA under award NNX12AK33A with the Universities Space Research Association
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Part of this work was completed while Oguz Bektas was an exchange visitor at the Universities Space Research Association at NASA Ames Research Center, whose kind hospitality is gratefully acknowledged.
Shankar Sankararaman performed this work while being employed at SGT Inc., NASA Ames Research Center, Moffett Field, CA 94035, USA.
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Bektas, O., Jones, J.A., Sankararaman, S. et al. A neural network filtering approach for similarity-based remaining useful life estimation. Int J Adv Manuf Technol 101, 87–103 (2019). https://doi.org/10.1007/s00170-018-2874-0
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DOI: https://doi.org/10.1007/s00170-018-2874-0