Autonomous and adaptive procedure for cumulative failure prediction

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.

[1]  Richard C.M. Yam,et al.  Intelligent Predictive Decision Support System for Condition-Based Maintenance , 2001 .

[2]  Alaa F. Sheta,et al.  Predicting Accumulated Faults in Software Testing Process Using Radial Basis Function Network Models , 2002, CATA.

[3]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[4]  Danilo P. Mandic,et al.  Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability , 2001 .

[5]  Peter W. Tse,et al.  Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks , 1999 .

[6]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[7]  A. Lapedes,et al.  Nonlinear Signal Processing Using Neural Networks , 1987 .

[8]  Sang-Un Lee,et al.  Neural Network Modeling for Software Reliability Prediction from Failure Time Data , 1999 .

[9]  Songcan Chen,et al.  Chained DLS-ICBP Neural Networks with Multiple Steps Time Series Prediction , 2004, Neural Processing Letters.

[10]  David Zhang,et al.  On the neural network approach in software reliability modeling , 2001, J. Syst. Softw..

[11]  H. C. Pusey,et al.  An assessment of turbomachinery condition monitoring and failure prognosis technology , 1999 .

[12]  Wilson Wang,et al.  An adaptive predictor for dynamic system forecasting , 2007 .

[13]  Kai-Yuan Cai,et al.  A critical review on software reliability modeling , 1991 .

[14]  Wan Azizun Wan Adnan,et al.  An integrated neural-fuzzy system of software reliability prediction , 1994, Proceedings of 1994 1st International Conference on Software Testing, Reliability and Quality Assurance (STRQA'94).

[15]  L. Darrell Whitley,et al.  Prediction of Software Reliability Using Connectionist Models , 1992, IEEE Trans. Software Eng..

[16]  Meng Joo Er,et al.  NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches , 2005, Fuzzy Sets Syst..

[17]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[18]  Wee Kheng Leow,et al.  Opening the neural network black box: an algorithm for extracting rules from function approximating artificial neural networks , 2000, ICIS.

[19]  C. Reeves,et al.  Beyond The Cox Model : Artificial Neural Networks For Survival Analysis Part II , 2006 .

[20]  Markus Voelter,et al.  State of the Art , 1997, Pediatric Research.

[21]  Ioannis B. Theocharis,et al.  A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation , 2007, Neurocomputing.

[22]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[23]  D. Bostwick,et al.  Prediction of individual patient outcome in cancer , 2001, Cancer.

[24]  Benoît Iung,et al.  Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system , 2008, Reliab. Eng. Syst. Saf..

[25]  Noureddine Zerhouni,et al.  Training the Recurrent neural network by the Fuzzy Min‐Max algorithm for fault prediction , 2009 .

[26]  L. Darrell Whitley,et al.  Using neural networks in reliability prediction , 1992, IEEE Software.

[27]  Y Shao,et al.  Prognosis of remaining bearing life using neural networks , 2000 .

[28]  Lev V. Utkin,et al.  A FUZZY SOFTWARE RELIABILITY MODEL WITH MULTIPLE-ERROR INTRODUCTION AND REMOVAL , 2002 .

[29]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[30]  Michael J. Roemer,et al.  Development of diagnostic and prognostic technologies for aerospace health management applications , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[31]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[32]  Thong Ngee Goh,et al.  A study of the connectionist models for software reliability prediction , 2003 .

[33]  R. Sitte.  Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration , 1999 .

[34]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[35]  Farid Atry,et al.  Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts , 2009 .

[36]  Rodrigo Fernandes de Mello,et al.  A novel approach for distributed application scheduling based on prediction of communication events , 2010, Future Gener. Comput. Syst..

[37]  Noureddine Zerhouni,et al.  Réseaux de neurones récurrents à fonctions de base radiales: RRFR Application au pronostic , 2002, Rev. d'Intelligence Artif..

[38]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[39]  Peng Wang,et al.  Fault prognostics using dynamic wavelet neural networks , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[40]  Marcello Farina,et al.  Forecasting peak air pollution levels using NARX models , 2009, Eng. Appl. Artif. Intell..

[41]  Czesław Cempel,et al.  Simple condition forecasting techniques in vibroacoustical diagnostics , 1987 .

[42]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[43]  Witold Pedrycz,et al.  Fuzzy prediction architecture using recurrent neural networks , 2009, Neurocomputing.

[44]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[45]  Giovanni Soda,et al.  Local Feedback Multilayered Networks , 1992, Neural Computation.

[46]  Wan Azizun Wan Adnan,et al.  Artificial neural network for software reliability assessment , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[47]  Gowrishankar,et al.  Neural network based BER prediction for 802.16e channel , 2007, 2007 15th International Conference on Software, Telecommunications and Computer Networks.

[48]  B. Samanta,et al.  Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit , 2009, Comput. Geosci..

[49]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[50]  Noureddine Zerhouni,et al.  Recurrent radial basis function network for time-series prediction , 2003 .

[51]  Danilo P. Mandic,et al.  Recurrent Neural Networks for Prediction , 2001 .

[52]  Noureddine Zerhouni,et al.  A neuro-fuzzy monitoring system: Application to flexible production systems , 2006, Comput. Ind..

[53]  J. A. Leonard,et al.  Radial basis function networks for classifying process faults , 1991, IEEE Control Systems.

[54]  M.J. Roemer,et al.  Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft] , 2002, Proceedings, IEEE Aerospace Conference.

[55]  Lin Ma,et al.  Condition Monitoring in Engineering Asset Management , 2007 .

[56]  John E. Moody,et al.  Fast adaptive k-means clustering: some empirical results , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[57]  George Vachtsevanos,et al.  Fault prognosis using dynamic wavelet neural networks , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[58]  Alaa F. Sheta,et al.  Prediction of software reliability: a comparison between regression and neural network non-parametric models , 2001, Proceedings ACS/IEEE International Conference on Computer Systems and Applications.