Top

Neural Computing and Applications

Published in:

01-03-2012 | Original Article

Autonomous and adaptive procedure for cumulative failure prediction

Authors: Ryad Zemouri, Noureddine Zerhouni

Published in: Neural Computing and Applications | Issue 2/2012

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

search-config

AI-assisted search

Off

Abstract

An autonomous adaptive reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available failure time data, Fuzzy Min–Max algorithm is used to globally optimize the number of the k Gaussian nodes. This technique allows determining and initializing the k-centers of the neural network architecture in an iterative way. The user does not have to define arbitrary some parameters. The optimized neural network architecture is then iteratively and dynamically reconfigured as new failure occurs. The performance of the proposed approach has been tested using sixteen real-time software failure data.

previous article Plane-Gaussian artificial neural network

next article An optimizing method of RBF neural network based on genetic algorithm

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!

inform now

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!

inform now

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!

inform now

This dataset is available on: https://www.thedacs.com/databases/sled/swrel.php.

Adnan WA, Yaacob MH (1994) An integrated neural-fuzzy system of software reliability prediction. In: Proceedings of the 1st international conference on software testing, reliability and quality assurance. pp 154–158

Adnan WA, Yaakob M, Anas R, Tamjis MR (2000) Artificial neural network for software reliability assessment. In: 2000 TENCON proceedings of intelligent systems and technologies for the new millennium. pp 446–451

Aljahdali SH, Sheta A, Rine D (2001) Prediction of software reliability: a comparison between regression and neural network non-parametric models. In: Proceedings of ACS/IEEE international conference on computer systems and applications. pp 470–473

Aljahdali SH, Sheta A, Rine D (2002) Predicting accumulated faults in software testing process using radial basis function network models. In: Proceedings of the 17th international conference on computers and their applications. pp 26–29

Andalib A, Atry F (2009) Multi-step ahead forecasts for electricity prices using NARX: a new approach, a critical analysis of one-step ahead forecasts. Energy Convers Manag (Elsevier) 50(3):739–747CrossRef

Barbounis TG, Theocharis JB (2007) A locally recurrent fuzzy neural network with application to wind speed prediction using spatial correlation. Neurocomputing 70(7–9):1525–1542CrossRef

Batko W (1984) Prediction method in technical diagnostics. Doctoral Thesis, Cracov Mining Academy

Bernauer E (1996) Les réseaux de neurones et l’aide au diagnostic : un modèle de neurones bouclés pour l’apprentissage de séquences temporelles, thèse de doctorat, LAAS/Toulouse

Bostwichk DG, Burke HB (2001) Prediction of individual patient outcome in cancer. Cancer 91(Supplement 8):1643–1646CrossRef

10.

Byington C, Roemer M, Kacprzynski G, Galie T (2002) Prognostic enhancements to diagnostic systems for improved condition-based maintenance. In: IEEE aerospace conference. Big Sky, USA

11.

Cai KY, Cai L, Wang WD, Yu ZY, Zhang D (2001) On the neural network approach in software reliability modeling. J Syst Softw 58(1):47–62CrossRef

12.

Cai KY, Wen CY, Zhang ML (1991) A critical review on software reliability modeling. Reliab Eng Syst Saf 32(3):357–371CrossRef

13.

Cempel C (1987) Simple condition forecasting techniques in vibroacoustical diagnostics. Mech Syst Signal Process 1:75–82MATHCrossRef

14.

Dai Q, Chen S (2005) Chained DLS-ICBP neural networks with multiple steps time series prediction. Neural Process Lett 21:95–107MathSciNetCrossRef

15.

Darken C, Moody J (1990) Fast adaptive k-means clustering: some empirical results. In: Proceeding of the international joint conference on neural networks, vol 2. San Diego, CA, USA

16.

De Gooijer JG, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22:443–473CrossRef

17.

Dodonov E, de Mello RF (2009) A novel approach for distributed application scheduling based on prediction of communication events. Futur Gener Comput Syst (Elsevier)

18.

Elman JL (1990) Finding structure in time. Cogn Sci 14:179–211CrossRef

19.

Frasconi P, Gori M, et Soda G (1992) Local feedback multilayered networks. Neural Comput 4:120–130CrossRef

20.

Gao Y, Meng Joo ER (2005) NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets Syst 150:331–350MATHCrossRef

21.

Gowrishankar Ramesh BHS, Satyanarayana PS (2007) Neural network based BER prediction for 802.16e channel. In: 15th international conference on software, telecommunications and computer networks, (SoftCOM 2007). pp 1–5. 27–29 Sept 2007

22.

Graves D, Pedrycz W (2009) Fuzzy prediction architecture using recurrent neural networks. Neurocomputing (Elsevier) 72(7–9): 1668–1678, March 2009. Advances in machine learning and computational intelligence—16th European symposium on artificial neural networks 2008, 16th European symposium on artificial neural networks 2008

23.

Aiwina H, Zhanga S, Tana ACC, Mathewa J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process (Elsevier) 23(3):724–739CrossRef

24.

Ho SL, Xie M, Goh TN (2003) A study of the connectionist models for software reliability prediction. Comput Math Appl 46(7):1037–1045MathSciNetMATHCrossRef

25.

Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510CrossRef

26.

Joshi R, Reeves C (2006) Beyond the Cox model: artificial neural networks for survival analysis part II. In: Proceedings of the eighteenth international conference on systems engineering

27.

Karunanithi N, Whitley D, Malaiya YK (1992) Prediction of software reliability using connectionist models. IEEE Trans Softw Eng 18(7):563–574CrossRef

28.

Karunanithi N, Whitley D, Malaiya YK (1992) Using neural networks in reliability prediction. IEEE Softw 9(4):53–59CrossRef

29.

Kazmierczak K (1983) Application of autoregressive prognostic techniques in diagnostics. In: Proceedings of the vehicle diagnostics conference. Tuczno, Poland

30.

Lapedes AS, Farber R (1987) Nonlinear signal processing using neural networks: prediction and system modeling. Los Technical Report, Alamos National Laboratory

31.

Lee J (2007) A systematic approach for developing and deploying advanced prognostics technologies and tools: methodology and applications. In: Proceedings of the second world congress on engineering asset management. Harrogate, UK, pp 1195–1206

32.

Leonard JA, Kramer MA (1991) Radial Basis Function Networks for classifying process faults. IEEE Control Syst Mag 11:31–38CrossRef

33.

Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79–88CrossRef

34.

Ma L (2007) Condition monitoring in engineering asset management. In: Proceedings of Asia-Pacific vibration conference. Sapporo, Hokkaido, Japan

35.

Mandic DP, Chambers JA (2001) Recurrent neural networks for prediction: learning algorithms and architectures and stability. Wiley, ChichesterCrossRef

36.

Muller A, Suhner MC, Iung B (2008) Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliab Eng Syst Saf 93:234–253CrossRef

37.

Palluat N, Racoceanu1 D, Zerhouni N (2006) A neuro-fuzzy monitoring system: Application to flexible production systems. Comput Ind (Elsevier) 57(6):528–538, August 2006, E-maintenance Special Issue

38.

Park JY, Lee SU, Park JH (1999) Neural network modeling for software reliability prediction from failure time data. J Electr Eng Inf Sci 4(4):533–538

39.

Pisoni E, Farina M, Carnevale C, Piroddi L (2009) Forcasting peak air pollution levels using NARX models. Eng Appl Artif Intell (Elsevier) 22:593–602CrossRef

40.

Pusey HC, Roemer MJ (1999) An assessment of turbomachinery condition monitoring and failure prognosis technology. Shock Vib Dig 31:365–371CrossRef

41.

Roemer MJ, Kacprzynski GJ, Nwadiogbu EO, Bloor G (2001) Development of diagnostic and prognostic technologies for aerospace health management applications. In: Proceedings of the IEEE aerospace conference. Big Sky, Montana, USA, pp 3139–3147

42.

Samantaa B, Bandopadhyay S (2009) Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit. Comput Geosci (Elsevier) 35(8):1592–1602

43.

Setiono R, Leow WK, Thong JYL (2000) Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks. In: Proceedings of international conference on information systems. Brisbane, Australia, pp 176–186

44.

Shao Y, Nezu K (2000) Prognosis of remaining bearing life using neural networks. In: Proceedings of the institution of mechanical engineers. Part I J Syst Control Eng 214(3):217–230

45.

Sitte R (1999) Comparison of software-reliability-growth predictions: neural networks vs. parametric-recalibration. IEEE Trans Reliab 48(3):285–291CrossRef

46.

Tickle AB, Andrews R, Golea M, Diederich J (1998) The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEE Trans Neural Netw 9:1057–1068CrossRef

47.

Tse P, Atherton D (1999) Prediction of machine deterioration using vibration based fault trends and recurrent neural networks. Trans ASME J Vib Acoust 121:355–362CrossRef

48.

Tsoukalas LH, Uhrig RE (1996) Fuzzy and neural approaches in engineering. practical aspects of using neural networks, chapter 11. Wiley, New York, pp 385–405

49.

Utkin LV, Gurov SV, Shubinsky MI (2002) A fuzzy software reliability model with multiple-error introduction and removal. Int J Reliab Qual Saf Eng 9(3):215–227CrossRef

50.

Vachtsevanos G, Lewis FL, Roemer M, Hess A, Wu B (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, HobokenCrossRef

51.

Wang P, Vachtsevanos G (2001) Fault prognostics using dynamic wavelet neural networks. Artif Intell Eng Design Anal Manuf 15:349–365MATHCrossRef

52.

Wang W (2007) An adaptive predictor for dynamic system forecasting. Mech Syst Signal Process 21:809–823CrossRef

53.

Wang WQ, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Process 18:813–831CrossRef

54.

Yam RCM, Tse PW, Li L, Tu P (2001) Intelligent predictive decision support system for condition-based maintenance. Int J Adv Manuf Technol 17:383–391CrossRef

55.

Zemouri R, Racoceanu D, Zerhouni N (2002) b–«Réseaux de neurones Récurrents à Fonction de base Radiales: RRFR/Application au pronostic». Revue d’Intelligence Artificielle, RSTI série RIA 16(03):307–338

56.

Zemouri R, Racoceanu D, Zerhouni N (2003) Recurrent Radial Basis Function network for time-series prediction. Eng Appl Artif Intell 16:453–463CrossRef

57.

Zemouri R, Racoceanu D, Zerhouni N, Minca E, Filip F (2009) Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction. In: AIP Conference proceedings—March 5, 2009—vol 1107, pp 85–90, Intelligent systems and automation: 2nd mediterranean conference on intelligent systems and automation (CISA’09 Zarzis, Tunisia, 23–25 Mars 2009)

58.

Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62CrossRef

Title: Autonomous and adaptive procedure for cumulative failure prediction
Authors: Ryad Zemouri
Noureddine Zerhouni
Publication date: 01-03-2012
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 2/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0585-7

Springer Professional

Abstract

Please log in to get access to your license.

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

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2012

An optimizing method of RBF neural network based on genetic algorithm

Pinning a stochastic neural network to the synchronous state

Fuzzy isomorphism theorems of soft rings

Short-term load forecasting of power systems by gene expression programming

Plane-Gaussian artificial neural network

A correlation-based ant miner for classification rule discovery

Premium Partner