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Prologue: Artificial Intelligence for Energy Transition Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

8. Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

verfasst von : Mohand Arab Djeziri, Samir Benmoussa, Enrico Zio

Erschienen in: Artificial Intelligence Techniques for a Scalable Energy Transition

Verlag: Springer International Publishing

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Abstract

Scientific research in the area of fault prognosis is increasingly focused on estimating the Remaining Useful Life of equipment, since its knowledge is a key input to the scheduling of Condition-Based and Predictive Maintenance. Several research studies have been directed to developing methods for modeling the trend of health indicators for Remaining Useful Life estimation, this paper makes a review of these approaches. Fault diagnosis methods sensitive to the progressive evolution of degradation phenomena are presented and their usability for fault prognosis is discussed. Then, methods for modeling the trends of health indicators are analyzed to highlight the selection criteria of the modeling methods, according to the available information on the operating conditions of the systems, and on the degradation phenomenon. Finally, some reflections are made regarding the elements that prevent the large-scale use of prognostics in industry today, and on the integration of prognostics in risk assessment and management.

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Literatur
1.
M. Abdel-Hameed, A gamma wear process. IEEE Trans. Reliab. 24(2), 152–153 (1975)
2.
K. Abid, M.S. Mouchaweh, L. Cornez, Fault prognostics for the predictive maintenance of wind turbines: state of the art, in In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 113–125 (2018)
3.
D. Adams, M. Nataraju, A nonlinear dynamical systems framework for structural diagnosis and prognosis. Int. J. Eng. Sci. 40(17), 1919–1941 (2002)
4.
J. Altmann, J. Mathew, Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis. Mech. Syst. Signal Process. 15(5), 963–977 (2001)
5.
D. An, N.H. Kim, J.H. Choi, Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliab. Eng. Syst. Saf. 133, 223–236 (2015)
6.
J. Bakker, J.V. Noortwijk, Inspection validation model for life-cycle analysis, in Proceedings of the 2nd International Conference on Bridge Maintenance, Safety and Management (IABMAS), pp. 18–22 (2004)
7.
P. Bangalore, L.B. Tjernberg, An artificial neural network approach for early fault detection of gearbox bearings. IEEE Trans. Smart Grid 6(2), 980–987 (2015)
8.
9.
D. Banjevic, A.K.S. Jardine, Calculation of reliability function and remaining useful life for a Markov failure time process. IMA J. Manag. Math. 17, 115–130 (2006)MathSciNetMATH
10.
P. Baraldi, G. Bonfanti, E. Zio, Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics. Mech. Syst. Signal Process. 102, 382–400 (2018)
11.
J. Barbot, M. Fliess, T. Floquet, An algebraic framework for the design of nonlinear observers with unknown inputs, in 46th IEEE Conference on Decision and Control, pp. 384–389 (2007)
12.
E. Bechhoefer, A. Bernhard, D. He, P. Banerjee, Use of hidden semi-Markov models in the prognostics of shaft failure, in Annual forum proceedings – American Helicopter Society, pp. 1330–1335 (2006)
13.
B. Bellali, A. Hazzab, I.K. Bousserhane, D. Lefebvre, Parameter estimation for fault diagnosis in nonlinear systems by ANFIS. Procedia Eng. 29, 2016–2021 (2012)
14.
S. Benmoussa, M.A. Djeziri, Remaining useful life estimation without needing for prior knowledge of the degradation features. IEEE IET Sci. Meas. Technol. 11(8), 1071–1078 (2017)
15.
S. Benmoussa, B.O. Bouamama, R. Merzouki, Bond graph approach for plant fault detection and isolation: application to intelligent autonomous vehicle. IEEE Trans. Autom. Sci. Eng. 11(2), 585–593 (2014)
16.
S. Bhat, D. Bernstein, Geometric homogeneity with applications to finite-time stability. Math. Control Signals Syst. 17, 101–127 (2000)MathSciNetMATH
17.
S. Bhat, D. Bernstein, Finite-time stability of continuous autonomous systems. SIAM J. Control. Optim. 38(3), 751–766 (2005)MathSciNetMATH
18.
M. Blanke, M. Kinnaert, J. Lunze, M. Staroswiecki, Diagnosis and Fault-Tolerant Control (Springer, Berlin, 2006)MATH
19.
C. Byington, M. Roemer, T. Galie, Prognostic enhancements to diagnostic systems for improved condition-based maintenance, in In Proceedings of IEEE Aerospace Conference (2002)
20.
F. Cadini, E. Zio, D. Avram, Model-based Monte Carlo state estimation for condition-based component replacement. Reliab. Eng. Syst. Saf. 94(3), 752–758 (2009)
21.
J. Chen, R. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems (Springer, Berlin, 1999)MATH
22.
A. Christer, W. Wang, J. Sharp, A state space condition monitoring model for furnace erosion prediction and replacement. Eur. J. Oper. Res. 101(1), 1–14 (1997)MATH
23.
E. Cinlar, E. Osman, Z. Bazant, Stochastic process for extrapolating concrete creep. J. Eng. Mech. Div. 103(6), 1069–1088 (1977)
24.
J. Coble, Merging data sources to predict remaining useful life – an automated method to identify prognostic parameters. Ph.D. Diss., University of Tennessee (2010)
25.
J. Coble, J. Hines, Identifying optimal prognostic parameters from data: a genetic algorithm approach, in Annual Conference of the Prognostics and Health Management Society (2009)
26.
D. Cox, H. Miller, The Theory of Stochastic Processes, vol. 134 (CRC Press, Boca Raton, 1977)MATH
27.
J. Cruz-Victoria, R. Martinez-Guerra, J. Rincon-Pasaye, On nonlinear systems diagnosis using differential and algebraic methods. J. Frankl. Inst. 345, 102–117 (2008)MathSciNetMATH
28.
S.A. Dahidi, F.D. Maio, P. Baraldi, E. Zio, Remaining useful life estimation in heterogeneous fleets working under variable operating conditions. Reliab. Eng. Syst. Saf. 3156, 109–124 (2016)
29.
M.J. Daigle, K. Goebel, A model-based prognostics approach applied to pneumatic valves. Int. J. Prognosis Health Manage. 2, 84–99 (2011)
30.
W. Danwei, Y. Ming, L. Chang, A. Shai, Model-based Health Monitoring of Hybrid Systems (Springer, Berlin, 2013)
31.
O. Djedidi, M.A. Djeziri, N. MSirdi, Data-driven approach for feature drift detection in embedded electronic devices, in IFAC Proceeding of the IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), pp. 964–969 (2018)
32.
O. Djedidi, M.A. Djezizi, S. Benmoussa, Failure prognosis of embedded systems based on temperature drift assessment, in Proceedings of the International Conference on Integrated Modeling and Analysis in Applied Control and Automation, pp. 11–16 (2019)
33.
M.A. Djeziri, A. Aitouch, B.O. Bouamama, Sensor fault detection of energetic system using modified parity space approach, in Proceeding of the IEEE Control and Decision Conference, pp. 2578–2583 (2007)
34.
M.A. Djeziri, S. Benmoussa, M. Ouladsine, B.O. Bouamama, Wavelet decomposition applied to fluid leak detection and isolation in presence of disturbances, in IEEE Proceeding of the 18th Mediterranean Conference on Control and Automation (MED), pp. 104–109 (2012)
35.
M. Djeziri, S. Benmoussa, L. Nguyen, N. MSirdi, Fault prognosis based on physical and stochastic models, in Proceeding of the 2016 European Control Conference (2016)
36.
37.
M. Dong, D. He, A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology. Mech. Syst. Signal Process. 21, 2248–2266 (2007)
38.
R. Engel, G. Kreisselmeier, A continuous-time observer which converges in finite time. IEEE Trans. Autom. Control 47(7), 1202–1204 (2002)MathSciNetMATH
39.
S. Ferreiro, A. Arnaiz, B. Sierra, I. Irigoien, Application of Bayesian networks in prognostics for a new integrated vehicle health management concept. Expert Syst. Appl. 39(7), 6402–6418 (2012)
40.
M. Fliess, Some basic structural properties of generalized linear systems. Syst. Control Lett. 15(5), 391–396 (1990)MathSciNetMATH
41.
M. Fliess, C. Join, H. Sira-Ramirez, Robust residual generation for linear fault diagnosis: an algebraic setting with examples. Int. J. Control. 77, 1223–1242 (2004)MathSciNetMATH
42.
D. Frangopol, M. Kallen, J.V. Noortwijk, Probabilistic models for life cycle performance of deteriorating structures: review and future directions. Prog. Struct. Eng. Mater. 6(4), 197–212 (2004)
43.
L. Fridman, Y. Shtessel, C. Edwards, X.G. Yan, Higher order sliding-mode observer for state estimation and input reconstruction in nonlinear systems. Int. J. Robust Nonlinear Control 18, 339–412 (2007)MathSciNetMATH
44.
D. Galar, U. Kumar, J. Lee, W. Zhao, Remaining useful life estimation using time trajectory tracking and support vector machines. J. Phys. Conf. Ser. 364(1), 012063 (2012)
45.
Z. Gao, C. Cecati, S.X. Ding, A survey of fault diagnosis and fault-tolerant techniques; part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans. Ind. Electron. 62(6), 3757–3767 (2015)
46.
Z. Gao, C. Cecati, S.X. Ding, A survey of fault diagnosis and fault-tolerant techniques; part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans. Ind. Electron. 62(6), 3768–3774 (2015)
47.
M.C. Garcia, M.A. Sanz-Bobi, J.D. Pico, SIMAP: intelligent system for predictive maintenance: application to the health condition monitoring of a wind turbine gearbox. Comput. Ind. 57(6), 552–568 (2006)
48.
J. Gertler, Fault detection and isolation using parity relations. Control. Eng. Pract. 5, 653–661 (1997)
49.
J.C. Gomez-Mancilla, L. Palacios-Pineda, V. Nosov, Software package evaluation for Lyapunov exponent and others features of signals evaluating the condition monitoring performance on nonlinear dynamic system. J. Energy Power Eng. 9(5), 443–551 (2015)
50.
D. Gucik-Derigny, Contribution au pronostic des systemes à base de modles : theorie et application. Ph.D. Thesis, Universite Paul Cezanne Aix-Marseille (2011)
51.
D. He, R. Li, J. Zhu, Plastic bearing fault diagnosis based on a two-step data mining approach. IEEE Trans. Ind. Electron 60(8), 3429–3440 (2013)
52.
W. He, Q. Miao, M. Azarian, M. Pecht, Health monitoring of cooling fan bearings based on wavelet filter. Mech. Syst. Signal Process 64–65, 149–161 (2015)
53.
Y. Hu, P. Baraldi, F.D. Maio, E. Zio, A particle filtering and kernel smoothing-based approach for new design component prognostics. Reliab. Eng. Syst. Saf. 134, 19–31 (2015)
54.
N. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. London A Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)MathSciNetMATH
55.
R. Isermann, Fault Diagnosis Systems (Springer, Berlin, 2006)MATH
56.
M. James, Finite time observers and observability, in Proceedings of IEEE Conference on Decision and Control (1990)
57.
A. Jardine, D. Lin, D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20(7), 1483–1510 (2006)
58.
M. Jha, G. Dauphin-Tanguy, B. Ould-Bouamama, Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework. Mech. Syst. Signal Process. 75, 301–329 (2016)
59.
M. Kan, A. Tan, J. Mathew, A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech. Syst. Signal Process. 62, 1–20 (2013)
60.
D.C. Karnopp, D. Margolis, R. Rosenberg, Systems Dynamics: A Unified Approach. (John Wiley, Hoboken, 1990)
61.
Y. Khellil, G. Graton, M.A. Djeziri, M. Ouladsine, Fault detection and isolation in marine diesel engines, a generic methodology, in IFAC Proceeding of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), pp. 964–969 (2012)
62.
J. Lawless, M. Crowder, Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Anal. 10(3), 213–227 (2004)MathSciNetMATH
63.
B.P. Leao, T. Yoneyama, G. Rocha, K.T. Fitzgibbon, Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit, in 2008 International Conference on Prognostics and Health Management, pp. 1–8 (2008)
64.
M. Lebold, M. Thurston, Open standards for condition-based maintenance and prognostic systems, in Maintenance and Reliability Conference, pp. 6–9 (2001)
65.
J. Lee, J. Ni, D. Djurdjanovic, H. Qiu, H. Liao, Intelligent prognostics tools and e-maintenance. Comput. Ind. 57(6), 476–489 (2006)
66.
J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, D. Siegel, Prognostics and health management design for rotary machinery systems – reviews, methodology and applications. Mech. Syst. Signal Process. 42(1), 314–334 (2014)
67.
C. Li, H. Lee, Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mech. Syst. Signal Process. 19(04), 836–846 (2005)
68.
G. Li, S.J. Qin, Y.J.D. Zhou, Reconstruction based fault prognosis for continuous processes. Control. Eng. Pract. 18(10), 1211–1219 (2010)
69.
C. Lim, D. Mba, Switching Kalman filter for failure prognostic. Mech. Syst. Signal Process. 52, 426–435 (2015)
70.
J. Lin, M. Zuo, Gearbox fault diagnosis using adaptive wavelet filter. Mech. Syst. Signal Process. 17(6), 1259–1269 (2003)
71.
F. Liu, Synthèse d’observateurs à entrées inconnues pour les systèmes non linéaires. Ph.D. Thesis, Université de Basse-Normandie (2007)
72.
J. Luo, M. Namburu, K. Pattipati, L. Qiao, M. Kawamoto, S. Chigusa, Model-based prognostic techniques, in Proceedings of IEEE Systems Readiness Technology Conference (2003), pp. 330–340
73.
M. Luschen, Derivation and application of nonlinear analytical redundancy techniques with applications to robotics. Ph.D. Thesis, Houston, TX (2001)
74.
F.D. Maio, J. Hu, P. Tse, M. Pecht, K. Tsui, E. Zio, Ensemble-approaches for clustering health status of oil sand pumps. Expert Syst. Appl. 39(5), 4847–4859 (2012)
75.
F.D. Maio, F. Antonello, E. Zio, Condition-based probabilistic safety assessment of a spontaneous steam generator tube rupture accident scenario. Nucl. Eng. Des. 326, 41–54 (2018)
76.
S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)MATH
77.
C. Marcelo, J.P. Fossatti, J.I. Terra, Fault diagnosis of induction motors based on FFT, in Fourier Transform-Signal Processing. InTech (2012)
78.
A. Mathur, K. Cavanaugh, K. Pattipati, P. Willett, T. Galie, Reasoning and modeling systems in diagnosis and prognosis, in Aerospace/Defense Sensing, Simulation, and Controls, International Society for Optics and Photonics (2001), pp. 194–203
79.
T. Menard, E. Moulay, W. Perruquetti, A global high-gain finite-time observer. IEEE Trans. Autom. Control 55(6), 1500–1506 (2010)MathSciNetMATH
80.
P. Menold, Finite time and asymptotic time state estimation for linear and nonlinear systems. Ph.D. Thesis, Institute for Systems and Automatic Control, University of Stuttgart, Allemagne (2004)
81.
P. Menold, R. Findeisen, F. Allgower, Finite time convergent observers for nonlinear systems, in Proceedings of the IEEE Conference on Decision and Control (2003)
82.
P. Menold, R. Findeisen, F. Allgöwer, Finite time convergent observers for linear time-varying systems, in Proceedings of the Mediterranean Conference on Control and Automation (2003)
83.
Q. Miao, V. Makis, Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models. Mech. Syst. Signal Process. 21(2), 840–855 (2007)
84.
M. Moisan, O. Bernard, Semi-global finite-time observers for nonlinear systems. Automatica 44(12), 3152–3156 (2008)MathSciNet
85.
E. Moulay, W. Perruquetti, Finite-time stability and stabilization of a class of continuous systems. J. Math. Anal. Appl. 323(2), 1430–1443 (2003)MathSciNetMATH
86.
E. Moulay, W. Perruquetti, Finite-time stability of nonlinear systems, in 42nd IEEE International Conference on Decision and Control (2003), pp. 3641–3646
87.
A. Muller, M. Suhner, B. Iung, Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliab. Eng. Syst. Saf. 93(2), 234–253 (2008)
88.
A. Nagy-Kiss, G. Schutz, J. Ragot, Parameter estimation for uncertain systems based on fault diagnosis using Takagi–Sugeno model. ISA Trans. 56, 65–74 (2015)
89.
L. Nguyen, M.A. Djeziri, B. Ananou, M. Ouladsine, J. Pinaton, Degradation modelling with operating mode changes, in IEEE International Conference on Prognostics and Health Management, Austin, TX (2015)
90.
L. Nguyen, M.A. Djeziri, B. Ananou, M. Ouladsine, J. Pinaton, Health indicator extraction for fault prognosis in discrete manufacturing processes. IEEE Trans. Semicond. Manuf. 28(3), 306–317 (2015)
91.
J.V. Noortwijk, A survey of the application of gamma processes in maintenance. Reliab. Eng. Syst. Saf. 94(1), 2–21 (2009)
92.
A. Oppenheim, R. Schafer, J. Buck, Discrete-Time Signal Processing, vol. 2 (Prentice-Hall, Englewood Cliffs, 1989)MATH
93.
M. Orchard, B. Wu, G. Vachtsevanos, A particle filtering framework for failure prognosis, in World Tribology Congress III, pp. 883–884 (American Society of Mechanical Engineers, New York, 2005)
94.
Y. Pan, J. Chen, L. Guo, Robust bearing performance degradation assessment method based on improved wavelet packet and support vector data description. Mech. Syst. Signal Process 23(3), 669–681 (2009)
95.
C. Park, W. Padgett, Accelerated degradation models for failure based on geometric Brownian motion and gamma processes. Lifetime Data Anal. 11(4), 511–527 (2005)MathSciNetMATH
96.
M.A. Patil, P. Tagade, K.S. Hariharan, S.M. Kolake, T. Song, T. Yeo, S. Doo, A novel multistage support vector machine based approach for li ion battery remaining useful life estimation. Appl. Energy 159(1), 285–297 (2015)
97.
T. Raff, F. Lachner, F. Allgower, A finite time unknown input observer for linear systems, in Mediterranean Conference on Control and Automation (2006)
98.
M. Roemer, G. Kacprzynski, M. Kawamoto, S. Chigusa, Advanced diagnostics and prognostics for gas turbine engine risk assessment, in IEEE, Aerospace Conference Proceedings, pp. 345–353 (2000)
99.
S. Sankaraman, Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mech. Syst. Signal Process. 52, 228–247 (2015)
100.
A. Saxena, J. Celaya, E. Balaban, B. Saha, S. Saha, K. Goebel, Metrics for evaluating performance of prognostic techniques, in International Conference on Prognostics and Health Management (PHM08), pp. 1–17 (2008)
101.
A. Saxena, J. Celaya, B. Saha, S. Saha, K. Goebel, On applying the prognostic performance metrics, in Annual Conference of the Prognostics and Health Management Society (2009)
102.
M. Schlechtingen, I.F. Santos, S. Achiche, Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: system description. Appl. Soft Comput. 13(1), 259–270 (2013)
103.
X. Si, W. Wang, C. Hu, D. Zhou, Remaining useful life estimation: a review on the statistical data driven approaches. Eur. J. Oper. Res. 213(1), 1–14 (2011)MathSciNet
104.
R. Singleton, E. Strangas, S. Aviyente, Extended Kalman filtering for remaining useful life estimation of bearings. IEEE Trans. Ind. Electron. 62(3), 1781–1790 (2015)
105.
K.L. Son, M. Fouladirad, A. Barros, E. Levrat, B. Iung, Remaining useful life estimation based on stochastic deterioration models: a comparative study. Reliab. Eng. Syst. Saf. 112, 165–175 (2013)
106.
J. Sun, H. Zuo, W. Wang, M. Pecht, Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model. Mech. Syst. Signal Process. 42(2), 396–407 (2014)
107.
J. Sun, H. Zuo, W. Wang, M. Pecht, Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model. Mech. Syst. Signal Process. 45(2), 396–407 (2015)
108.
K. Tidriri, N. Chatti, S. Verron, T. Tiplica, Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: a review of researches and future challenges. Annu. Rev. Control. 42, 63–81 (2016)
109.
D. Tobon-Mejia, K. Medjaher, N. Zerhouni, The ISO 13381-1 standard’s failure prognostics process through an example, in IEEE Prognostics and Health Management Conference (PHM’10) (2010), pp. 1–12
110.
S. Tseng, C. Peng, Stochastic diffusion modeling of degradation data. J. Data Sci. 5(3), 315–333 (2007)
111.
S. Tseng, J. Tang, I. Ku, Determination of burn-in parameters and residual life for highly reliable products. Nav. Res. Logist. 50, 1–14 (2003)MathSciNetMATH
112.
D. Van, C. Berenguer, Condition based maintenance model for a production deteriorating system, in Conference on Control and Fault-Tolerant Systems (2010)
113.
X. Wang, Wiener processes with random effects for degradation data. J. Multivar. Anal. 101(2), 340–351 (2010)MathSciNetMATH
114.
P. Wang, G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks. Technical Report. AAAI Technical Report (1999)
115.
A.P. Wang, H. Wang, Fault diagnosis for nonlinear systems via neural networks and parameter estimation, in 2005 International Conference on Control and Automation, vol. 1 (2005), pp. 559–563
116.
W. Wang, M. Carr, W. Xu, K. Kobbacy, A model for residual life prediction based on Brownian motion with an adaptive drift. Microelectron. Reliab. 51(2), 285–293 (2011)
117.
W. Wu, J. Hu, J. Zhang, Prognostics of machine health condition using an improved ARIMA-based prediction method, in 2nd IEEE Conference on Industrial Electronics and Applications (2007), pp. 1062–1067
118.
J. Yan, M. Koc, J. Lee, A prognostic algorithm for machine performance assessment and its application. Prod. Plan. Control 15(8), 796–801 (2004)
119.
D. Yu, J. Cheng, Y. Yang, Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech. Syst. Signal Process. 19(2), 259–270 (2005)
120.
Y. Zhang, R.H.H. Xiong, M.G. Pecht, Lithium-ion battery remaining useful life prediction with box–cox transformation and Monte Carlo simulation. IEEE Trans. Ind. Electron. 66(2), 1585–1597 (2018)
121.
Y. Zhang, R.H.H. Xiong, M.G. Pecht, Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Trans. Veh. Technol. 67(7), 5695–5705 (2018)
122.
123.
E. Zio, Challenges in the vulnerability and risk analysis of critical infrastructures. Reliab. Eng. Syst. Saf. 152, 137–150 (2016)
124.
E. Zio, F.D. Maio, M. Stasi, A data-driven approach for predicting failure scenarios in nuclear systems. Ann. Nucl. Energy 37, 482–491 (2010)
125.
Z. Zng, E. Zio, A classification-based framework for trustworthiness assessment of quantitative risk analysis. Saf. Sci. 99, 1215–226 (2017)
Metadaten
Titel
Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation
verfasst von
Mohand Arab Djeziri
Samir Benmoussa
Enrico Zio
Copyright-Jahr
2020
Buch
Artificial Intelligence Techniques for a Scalable Energy Transition

Print ISBN: 978-3-030-42725-2

Electronic ISBN: 978-3-030-42726-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-42726-9_8

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