Skip to main content
Top
Published in: Neural Computing and Applications 18/2020

10-09-2019 | Extreme Learning Machine and Deep Learning Networks

Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes

Authors: Jian-Ming Bai, Guang-She Zhao, Hai-Jun Rong

Published in: Neural Computing and Applications | Issue 18/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper aims to improve data-driven prognostics by presenting a novel approach of directly estimating the remaining useful life (RUL) of aero-engines without requiring setting any failure threshold information or estimating degradation states. Specifically, based on the sensory data, RUL estimations are directly obtained through the universal function approximation capability of the extreme learning machine (ELM) algorithm. To achieve this, the features related with the RUL are first extracted from the sensory data as the inputs of the ELM model. Besides, to optimize the number of observed sensors, three evaluation metrics of correlation, monotonicity and robustness are defined and combined to automatically select the most relevant sensor values for more effective and efficient remaining useful life predictions. The validity and superiority of the proposed approach is evaluated by the widely used turbofan engine datasets from NASA Ames prognostics data repository. The proposed approach shows improved RUL estimation applicability at any time instant of the degradation process without determining the failure thresholds. This also simplifies the RUL estimation procedure. Moreover, the random properties of hidden nodes in the ELM learning mechanisms ensures the simplification and efficiency for real-time implementation. Therefore, the proposed approach suits to real-world applications in which prognostics estimations are required to be fast.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Altay A, Ozkan O, Kayakutlu G (2015) Prediction of aircraft failure times using artificial neural networks and genetic algorithms. J Aircr 51(1):47–53 Altay A, Ozkan O, Kayakutlu G (2015) Prediction of aircraft failure times using artificial neural networks and genetic algorithms. J Aircr 51(1):47–53
2.
go back to reference An D, Kim NH, Choi JH (2013) Options for prognostics methods: a review of data-driven and physics-based prognostics. In: 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Boston, America, pp 1–19 An D, Kim NH, Choi JH (2013) Options for prognostics methods: a review of data-driven and physics-based prognostics. In: 54th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference, Boston, America, pp 1–19
3.
go back to reference Babu GS, Zhao P, Li XL (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. In: International conference on database systems for advanced applications, Springer, Cham, pp 214–228 Babu GS, Zhao P, Li XL (2016) Deep convolutional neural network based regression approach for estimation of remaining useful life. In: International conference on database systems for advanced applications, Springer, Cham, pp 214–228
4.
go back to reference Balaban E, Saxena A, Narasimhan S, Roychoudhury I, Koopmans M, Ott C, Goebel K (2015) Prognostic health-management system development for electromechanical actuators. J Aerosp Inf Syst 12(3):329–344 Balaban E, Saxena A, Narasimhan S, Roychoudhury I, Koopmans M, Ott C, Goebel K (2015) Prognostic health-management system development for electromechanical actuators. J Aerosp Inf Syst 12(3):329–344
5.
go back to reference Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. Publ Am Stat Assoc 74(368):829–836MathSciNetMATH Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. Publ Am Stat Assoc 74(368):829–836MathSciNetMATH
6.
go back to reference Cui Y, Shi J, Wang Z (2016) Quantum assimilation-based state-of-health assessment and remaining useful life estimation for electronic systems. IEEE Trans Ind Electron 63(4):2379–2390 Cui Y, Shi J, Wang Z (2016) Quantum assimilation-based state-of-health assessment and remaining useful life estimation for electronic systems. IEEE Trans Ind Electron 63(4):2379–2390
7.
go back to reference Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. Complex Intell Syst 2(2):125–154 Elattar HM, Elminir HK, Riad AM (2016) Prognostics: a literature review. Complex Intell Syst 2(2):125–154
8.
go back to reference Fulcher BD, Little MA, Jones NS (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods. J R Soc Interface 10(83):20130048 Fulcher BD, Little MA, Jones NS (2013) Highly comparative time-series analysis: the empirical structure of time series and their methods. J R Soc Interface 10(83):20130048
9.
go back to reference Gugulothu N, TV V, Malhotra P, Vig L, Agarwal P, Shroff G (2018) Predicting remaining useful life using time series embeddings based on recurrent neural networks. Int J Progn Health Manag 9(1):1–10 Gugulothu N, TV V, Malhotra P, Vig L, Agarwal P, Shroff G (2018) Predicting remaining useful life using time series embeddings based on recurrent neural networks. Int J Progn Health Manag 9(1):1–10
10.
go back to reference Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V (2006) Extreme learning machine for predicting HLA-peptide binding. Lect Notes Comput Sci 3973:716–721 Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V (2006) Extreme learning machine for predicting HLA-peptide binding. Lect Notes Comput Sci 3973:716–721
11.
go back to reference Haque MS, Choi S, Baek J (2018) Auxiliary particle filtering-based estimation of remaining useful life of igbt. IEEE Trans Ind Electron 65(3):2693–2703 Haque MS, Choi S, Baek J (2018) Auxiliary particle filtering-based estimation of remaining useful life of igbt. IEEE Trans Ind Electron 65(3):2693–2703
12.
go back to reference Heimes FO, Systems B (2008) Recurrent neural networks for remaining useful life estimation. In: 2008 international conference on prognostics and health management, Denver, USA, pp 1–6 Heimes FO, Systems B (2008) Recurrent neural networks for remaining useful life estimation. In: 2008 international conference on prognostics and health management, Denver, USA, pp 1–6
13.
go back to reference Huang GB (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281MathSciNet Huang GB (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281MathSciNet
14.
go back to reference Huang GB, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Netw 9(1):224–229 Huang GB, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Netw 9(1):224–229
15.
go back to reference Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529 Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513–529
16.
go back to reference Huang GB, Zhu QY, Siew CK (2006) Real-time learning capability of neural networks. IEEE Trans Neural Netw 17(4):863–878 Huang GB, Zhu QY, Siew CK (2006) Real-time learning capability of neural networks. IEEE Trans Neural Netw 17(4):863–878
17.
go back to reference Ibrahim M, Steiner NY, Jemei S, Hissel D (2016) Wavelet-based approach for online fuel cell remaining useful lifetime prediction. IEEE Trans Ind Electron 63(8):5057–5068 Ibrahim M, Steiner NY, Jemei S, Hissel D (2016) Wavelet-based approach for online fuel cell remaining useful lifetime prediction. IEEE Trans Ind Electron 63(8):5057–5068
18.
go back to reference Iverson DL, Martin R, Schwabacher M, Spirkovska L, Taylor W, Mackey R, Castle JP, Baskaran V (2012) General purpose data-driven monitoring for space operations. J Aerosp Comput Inf Commun 9(2):26–44 Iverson DL, Martin R, Schwabacher M, Spirkovska L, Taylor W, Mackey R, Castle JP, Baskaran V (2012) General purpose data-driven monitoring for space operations. J Aerosp Comput Inf Commun 9(2):26–44
19.
go back to reference Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510 Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510
20.
go back to reference Javed K, Gouriveau R, Zerhouni N (2013) Novel failure prognostics approach with dynamic thresholds for machine degradation. In: 39th annual conference of the IEEE industrial electronics society (IECON), Vienna, Austria, pp 4404–4409 Javed K, Gouriveau R, Zerhouni N (2013) Novel failure prognostics approach with dynamic thresholds for machine degradation. In: 39th annual conference of the IEEE industrial electronics society (IECON), Vienna, Austria, pp 4404–4409
21.
go back to reference Javed K, Gouriveau R, Zerhouni N (2015) A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Trans Cybern 45(12):2626–2639 Javed K, Gouriveau R, Zerhouni N (2015) A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Trans Cybern 45(12):2626–2639
22.
go back to reference Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107(1):241–265 Khan S, Yairi T (2018) A review on the application of deep learning in system health management. Mech Syst Signal Process 107(1):241–265
23.
go back to reference Khelif R, Chebel-Morello B, Malinowski S, Laajili E, Fnaiech F, Zerhouni N (2017) Direct remaining useful life estimation based on support vector regression. IEEE Trans Ind Electron 64(3):2276–2285 Khelif R, Chebel-Morello B, Malinowski S, Laajili E, Fnaiech F, Zerhouni N (2017) Direct remaining useful life estimation based on support vector regression. IEEE Trans Ind Electron 64(3):2276–2285
24.
go back to reference Khelif R, Malinowski S, Chebel-Morello B, Zerhouni N (2014) RUL prediction based on a new similarity-instance based approach. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE), Istanbul, Turkey, pp 2463–2468 Khelif R, Malinowski S, Chebel-Morello B, Zerhouni N (2014) RUL prediction based on a new similarity-instance based approach. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE), Istanbul, Turkey, pp 2463–2468
25.
go back to reference Li N, Lei Y, Guo L, Yan T, Lin J (2017) Remaining useful life prediction based on a general expression of stochastic process models. IEEE Trans Ind Electron 64(7):5709–5718 Li N, Lei Y, Guo L, Yan T, Lin J (2017) Remaining useful life prediction based on a general expression of stochastic process models. IEEE Trans Ind Electron 64(7):5709–5718
26.
go back to reference Li N, Lei Y, Lin J, Ding SX (2015) An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans Ind Electron 62(12):7762–7773 Li N, Lei Y, Lin J, Ding SX (2015) An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans Ind Electron 62(12):7762–7773
27.
go back to reference Liang NY, Saratchandran P, Huang GB, Sundararajan N (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16(1):29–38 Liang NY, Saratchandran P, Huang GB, Sundararajan N (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16(1):29–38
28.
go back to reference Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61(5):2464–2472 Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61(5):2464–2472
29.
go back to reference Liu X, Song P, Yang C, Hao C, Peng W (2018) Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation. IEEE Trans Ind Electron 65(2):1549–1558 Liu X, Song P, Yang C, Hao C, Peng W (2018) Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation. IEEE Trans Ind Electron 65(2):1549–1558
30.
go back to reference MacIsaac B, Langton R (eds) (2011) Prognostics and health monitoring systems. Wiley, Hoboken MacIsaac B, Langton R (eds) (2011) Prognostics and health monitoring systems. Wiley, Hoboken
31.
go back to reference Malhotra P, TV V, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) Multi-sensor prognostics using an unsupervised health index based on lstm encoder–decoder. In: 1st ACM SIGKDD workshop on machine learning for prognostics and health management. San Francisco, USA Malhotra P, TV V, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) Multi-sensor prognostics using an unsupervised health index based on lstm encoder–decoder. In: 1st ACM SIGKDD workshop on machine learning for prognostics and health management. San Francisco, USA
32.
go back to reference Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, 5th edn. Wiley, HobokenMATH Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, 5th edn. Wiley, HobokenMATH
33.
go back to reference Pecht M (2009) Prognostics and health management of electronics. In: Boller C, Chang FK, Fujino Y (eds) Encyclopedia of structural health monitoring, chap 150. Wiley, Hoboken, pp 222–229 Pecht M (2009) Prognostics and health management of electronics. In: Boller C, Chang FK, Fujino Y (eds) Encyclopedia of structural health monitoring, chap 150. Wiley, Hoboken, pp 222–229
34.
go back to reference 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 of the prognostics and health management society 2014, Nantes, France, pp 1–18 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 of the prognostics and health management society 2014, Nantes, France, pp 1–18
35.
go back to reference Ramasso E, Gouriveau R (2010) Prognostics in switching systems: evidential markovian classification of real-time neuro-fuzzy predictions. In: 2010 prognostics and system health management conference, Macao, China, pp 1–10 Ramasso E, Gouriveau R (2010) Prognostics in switching systems: evidential markovian classification of real-time neuro-fuzzy predictions. In: 2010 prognostics and system health management conference, Macao, China, pp 1–10
36.
go back to reference Ramasso E, Rombaut M, Zerhouni N (2013) Joint prediction of continuous and discrete states in time-series based on belief functions. IEEE Trans Cybern 43(1):37–50 Ramasso E, Rombaut M, Zerhouni N (2013) Joint prediction of continuous and discrete states in time-series based on belief functions. IEEE Trans Cybern 43(1):37–50
37.
go back to reference Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for CMAPSS datasets. Int J Progn Health Manag 5(2):1–15 Ramasso E, Saxena A (2014) Performance benchmarking and analysis of prognostic methods for CMAPSS datasets. Int J Progn Health Manag 5(2):1–15
38.
go back to reference Rao C, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, HobokenMATH Rao C, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, HobokenMATH
39.
go back to reference Rong HJ, Bai JM, Bai JM, Zhao GS, Liang YQ (2015) Adaptive neural control for a class of MIMO nonlinear systems with extreme learning machine. Neurocomputing 149((PA)):405–414 Rong HJ, Bai JM, Bai JM, Zhao GS, Liang YQ (2015) Adaptive neural control for a class of MIMO nonlinear systems with extreme learning machine. Neurocomputing 149((PA)):405–414
40.
go back to reference Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B Cybern 39(4):1067–1072 Rong HJ, Huang GB, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern Part B Cybern 39(4):1067–1072
41.
go back to reference Rong HJ, Suresh S, Zhao GS (2011) Stable indirect adaptive neural controller for a class of nonlinear system. Neurocomputing 74(16):2582–2590 Rong HJ, Suresh S, Zhao GS (2011) Stable indirect adaptive neural controller for a class of nonlinear system. Neurocomputing 74(16):2582–2590
42.
go back to reference Rong HJ, Zhao GS (2013) Direct adaptive neural control of nonlinear systems with extreme learning machine. Neural Comput Appl 22(3–4):577–586 Rong HJ, Zhao GS (2013) Direct adaptive neural control of nonlinear systems with extreme learning machine. Neural Comput Appl 22(3–4):577–586
44.
go back to reference Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 international conference on prognostics and health management, Denver, America, pp 1–9 Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 international conference on prognostics and health management, Denver, America, pp 1–9
45.
go back to reference Saxena A, Roychoudhury I, Celaya JR, Saha B, Saha S (2012) Requirements flowdown for prognostics and health management. In: AIAA Infotech@aerospace conference, Garden Grove, America, pp 1–13 Saxena A, Roychoudhury I, Celaya JR, Saha B, Saha S (2012) Requirements flowdown for prognostics and health management. In: AIAA Infotech@aerospace conference, Garden Grove, America, pp 1–13
46.
go back to reference Saxena A, Roychoudhury I, Celaya JR, Saha S, Saha B, Goebel K (2010) Requirements specifications for prognostics: An overview. In: AIAA Infotech@Aerospace Conference, pp. 1–13. Atlanta, America Saxena A, Roychoudhury I, Celaya JR, Saha S, Saha B, Goebel K (2010) Requirements specifications for prognostics: An overview. In: AIAA Infotech@Aerospace Conference, pp. 1–13. Atlanta, America
47.
go back to reference Saxena A, Roychoudhury I, Celaya JR, Saha S, Saha B, Goebel K (2010) Requirements specifications for prognostics: an overview. In: AIAA Infotech@Aerospace conference, Atlanta, America, pp 1–13 Saxena A, Roychoudhury I, Celaya JR, Saha S, Saha B, Goebel K (2010) Requirements specifications for prognostics: an overview. In: AIAA Infotech@Aerospace conference, Atlanta, America, pp 1–13
48.
go back to reference Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23(7):1149–1157 Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23(7):1149–1157
49.
go back to reference Xu JX, Wang W, Goh JCH, Lee G (2005) Internal model approach for gait modeling and classification. In: The 27th annual international conference of the IEEE engineering in medicine and biology society (EMBS). Shanghai, China, 1–4 September 2005 Xu JX, Wang W, Goh JCH, Lee G (2005) Internal model approach for gait modeling and classification. In: The 27th annual international conference of the IEEE engineering in medicine and biology society (EMBS). Shanghai, China, 1–4 September 2005
50.
go back to reference Yang F, Habibullah MS, Zhang T, Xu Z, Lim P (2016) Health index-based prognostics for remaining useful life predictions in electrical machines. IEEE Trans Ind Electron 63(4):2633–2644 Yang F, Habibullah MS, Zhang T, Xu Z, Lim P (2016) Health index-based prognostics for remaining useful life predictions in electrical machines. IEEE Trans Ind Electron 63(4):2633–2644
51.
go back to reference Yang J, Xu Y, Rong HJ, Du S, Chen B (2018) Sparse recursive least mean p-power extreme learning machine for regression. IEEE Access PP(99):1–1 Yang J, Xu Y, Rong HJ, Du S, Chen B (2018) Sparse recursive least mean p-power extreme learning machine for regression. IEEE Access PP(99):1–1
52.
go back to reference Yang J, Ye F, Rong HJ, Chen B (2017) Recursive least mean p-power extreme learning machine. Neural Networks 91:22–33MATH Yang J, Ye F, Rong HJ, Chen B (2017) Recursive least mean p-power extreme learning machine. Neural Networks 91:22–33MATH
53.
go back to reference Yeu CWT, Lim MH, Huang GB, Agarwal A, Ong YS (2006) A new machine learning paradigm for terrain reconstruction. IEEE Geosci Remote Sens Lett 3(3):382–386 Yeu CWT, Lim MH, Huang GB, Agarwal A, Ong YS (2006) A new machine learning paradigm for terrain reconstruction. IEEE Geosci Remote Sens Lett 3(3):382–386
54.
go back to reference Yin S, Ding SX, Xie X, Luo H (2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron 61(11):6418–6428 Yin S, Ding SX, Xie X, Luo H (2014) A review on basic data-driven approaches for industrial process monitoring. IEEE Trans Ind Electron 61(11):6418–6428
55.
go back to reference Zhai Q, Ye ZS (2017) RUL prediction of deteriorating products using an adaptive wiener process model. IEEE Trans Ind Inf 13(6):2911–2921 Zhai Q, Ye ZS (2017) RUL prediction of deteriorating products using an adaptive wiener process model. IEEE Trans Ind Inf 13(6):2911–2921
56.
go back to reference Zhang B, Zhang L, Xu J (2016) Degradation feature selection for remaining useful life prediction of rolling element bearings. Qual Reliab Eng Int 32(2):547–554 Zhang B, Zhang L, Xu J (2016) Degradation feature selection for remaining useful life prediction of rolling element bearings. Qual Reliab Eng Int 32(2):547–554
57.
go back to reference Zhu K, Liu T (2018) Online tool wear monitoring via hidden semi-markov model with dependent durations. IEEE Trans Ind Inf 14(1):69–78 Zhu K, Liu T (2018) Online tool wear monitoring via hidden semi-markov model with dependent durations. IEEE Trans Ind Inf 14(1):69–78
58.
go back to reference Zio E, Maio FD (2010) A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliab Eng Syst Saf 95(1):49–57 Zio E, Maio FD (2010) A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system. Reliab Eng Syst Saf 95(1):49–57
Metadata
Title
Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes
Authors
Jian-Ming Bai
Guang-She Zhao
Hai-Jun Rong
Publication date
10-09-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 18/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04478-1

Other articles of this Issue 18/2020

Neural Computing and Applications 18/2020 Go to the issue

Deep Learning Approaches for RealTime Image Super Resolution (DLRSR)

CASR: a context-aware residual network for single-image super-resolution

Premium Partner