Predicting component reliability and level of degradation with complex-valued neural networks

In this paper, multilayer feedforward neural networks based on multi-valued neurons (MLMVN), a specific type of complex valued neural networks, are proposed to be applied to reliability and degradation prediction problems, formulated as time series. MLMVN have demonstrated their ability to extract complex dynamic patterns from time series data for mid- and long-term predictions in several applications and benchmark studies. To the authors' knowledge, it is the first time that MLMVN are applied for reliability and degradation prediction. MLMVN are applied to a case study of predicting the level of degradation of railway track turnouts using real data. The performance of the algorithms is first evaluated using benchmark study data. The results obtained in the reliability prediction study of the benchmark data show that MLMVN outperform other machine learning algorithms in terms of prediction precision and are also able to perform multi-step ahead predictions, as opposed to the previously best performing benchmark studies which only performed up to two-step ahead predictions. For the railway turnout application, MLMVN confirm the good performance in the long-term prediction of degradation and do not show accumulating errors for multi-step ahead predictions.

[1]  M. Nikulin,et al.  Estimation in Degradation Models with Explanatory Variables , 2001, Lifetime data analysis.

[2]  B. Kaushik,et al.  Achieving maximum reliability in fault tolerant network design for variable networks , 2013, Appl. Soft Comput..

[3]  Gregory Levitin,et al.  Robust recurrent neural network modeling for software fault detection and correction prediction , 2007, Reliab. Eng. Syst. Saf..

[4]  Kuan-Yu Chen,et al.  Forecasting systems reliability based on support vector regression with genetic algorithms , 2007, Reliab. Eng. Syst. Saf..

[5]  Zainal Ahmad,et al.  Optimum parameters for fault detection and diagnosis system of batch reaction using multiple neural networks , 2012 .

[6]  Enrico Zio,et al.  Failure and reliability prediction by support vector machines regression of time series data , 2011, Reliab. Eng. Syst. Saf..

[7]  Marizan Sulaiman,et al.  Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System , 2012 .

[8]  Mustapha Nourelfath,et al.  Quantized hopfield networks for reliability optimization , 2003, Reliab. Eng. Syst. Saf..

[9]  S. Fararooy,et al.  On-line condition monitoring of railway equipment using neural networks , 1995 .

[10]  Mohamad Khalil,et al.  Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues , 2013, Int. J. Mach. Learn. Cybern..

[11]  Jorge E. Hurtado,et al.  Neural-network-based reliability analysis: a comparative study , 2001 .

[12]  Pingfeng Wang,et al.  Failure diagnosis using deep belief learning based health state classification , 2013, Reliab. Eng. Syst. Saf..

[13]  Jacek M. Zurada,et al.  Blur Identification by Multilayer Neural Network Based on Multivalued Neurons , 2008, IEEE Transactions on Neural Networks.

[14]  Benoît Iung,et al.  Remaining useful life estimation based on stochastic deterioration models: A comparative study , 2013, Reliab. Eng. Syst. Saf..

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Stanisław Radkowski,et al.  ON-LINE CONDITION MONITORING OF A POWER TRANSMISSION UNIT OF A RAIL VEHICLE , 2003 .

[17]  Loon Ching Tang,et al.  Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..

[18]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[19]  Jian-Bo Yang,et al.  System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization , 2010, Expert Syst. Appl..

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

[21]  Jongsoo Lee,et al.  Reliability assessment using feed-forward neural network-based approximate meta-models , 2012 .

[22]  Zhongbao Zhou,et al.  A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries in spacecraft , 2013, Reliab. Eng. Syst. Saf..

[23]  S. Lolas,et al.  Prediction of vehicle reliability performance using artificial neural networks , 2008, Expert Syst. Appl..

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

[25]  Jaques Reifman,et al.  Survey of Artificial Intelligence Methods for Detection and Identification of Component Faults in Nuclear Power Plants , 1997 .

[26]  Chao Hu,et al.  Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[27]  Ping-Feng Pai,et al.  System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..

[28]  Nader Nariman-Zadeh,et al.  Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA) , 2013, Eng. Appl. Artif. Intell..

[29]  Igor N. Aizenberg,et al.  Complex-Valued Neural Networks with Multi-Valued Neurons , 2011, Studies in Computational Intelligence.

[30]  A. F. Stronach,et al.  Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks , 2002 .

[31]  Enrico Zio,et al.  A particle swarm‐optimized support vector machine for reliability prediction , 2012, Qual. Reliab. Eng. Int..

[32]  Claudio Moraga,et al.  Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm , 2006, Soft Comput..

[33]  Tongmin Jiang,et al.  Reliability prediction based on degradation measure distribution and wavelet neural network , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[34]  Mehmet Sevkli,et al.  A Simple State-Based Prognostic Model for Railway Turnout Systems , 2011, IEEE Transactions on Industrial Electronics.

[35]  Paulo F. Frutuoso e Melo,et al.  NAROAS: a neural network-based advanced operator support system for the assessment of systems reliability , 2005, Reliab. Eng. Syst. Saf..

[36]  Enrico Zio,et al.  Failure and Reliability Predictions by Infinite Impulse Response Locally Recurrent Neural Networks , 2012 .

[37]  Claudio M. Rocco Sanseverino.  Singular spectrum analysis and forecasting of failure time series , 2013, Reliab. Eng. Syst. Saf..

[38]  Abdullah Konak,et al.  Estimation of all-terminal network reliability using an artificial neural network , 2002, Comput. Oper. Res..

[39]  S ClaudioM.Rocco,et al.  Singular spectrum analysis and forecasting of failure time series , 2013 .

[40]  Yu Peng,et al.  A modified echo state network based remaining useful life estimation approach , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[41]  Asoke K. Nandi,et al.  Modified self-organising map for automated novelty detection applied to vibration signal monitoring , 2006 .

[42]  Ahmed Z. Al-Garni,et al.  Artificial neural network application of modeling failure rate for Boeing 737 tires , 2011, Qual. Reliab. Eng. Int..

[43]  Snehamoy Chatterjee,et al.  Reliability estimation using a genetic algorithm-based artificial neural network: An application to a load-haul-dump machine , 2012, Expert Syst. Appl..

[44]  Tim Kelly,et al.  Using fuzzy self-organising maps for safety critical systems , 2007, Reliab. Eng. Syst. Saf..

[45]  Enrico Zio,et al.  A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems , 2007, Reliab. Eng. Syst. Saf..

[46]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[47]  Carlos Guedes Soares,et al.  Artificial neural network model of the strength of thin rectangular plates with weld induced initial imperfections , 2011, Reliab. Eng. Syst. Saf..

[48]  Mamoun Suliman,et al.  Neural network realization of Markov reliability and fault-tolerance models , 1991 .

[49]  Mohsen Khatibinia,et al.  Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine , 2013, Reliab. Eng. Syst. Saf..

[50]  David W. Coit,et al.  Neural Network Models to Anticipate Failures of Airport Ground Transportation Vehicle Doors , 2010, IEEE Transactions on Automation Science and Engineering.

[51]  David W. Coit,et al.  A neural network applied to estimate Burr XII distribution parameters , 2010, Reliab. Eng. Syst. Saf..

[52]  T. G. Habetler,et al.  Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics , 2002 .

[53]  Clive Roberts,et al.  Industrial fault diagnosis: Pneumatic train door case study , 2002 .

[54]  Hong-Zhong Huang,et al.  Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment , 2013, Int. J. Syst. Sci..

[55]  Zhigang Tian,et al.  A neural network approach for remaining useful life prediction utilizing both failure and suspension histories , 2010 .

[56]  Jun Du,et al.  Layered clustering multi-fault diagnosis for hydraulic piston pump , 2013 .

[57]  Bo-Suk Yang,et al.  Combined Probability Approach and Indirect Data-Driven Method for Bearing Degradation Prognostics , 2011, IEEE Transactions on Reliability.

[58]  G. N. Marichal,et al.  Extraction of rules for faulty bearing classification by a Neuro-Fuzzy approach , 2011 .

[59]  Jacques Wainer,et al.  A COMBINATION OF SUPPORT VECTOR MACHINE AND k-NEAREST NEIGHBORS FOR MACHINE FAULT DETECTION , 2013, Appl. Artif. Intell..