Abstract
This chapter presents development of enabling technologies that are able to assess the reliability of remanufactured products based on predictive modeling methods, to describe fast and accurate prediction algorithms that are able to predict condition of critical components or parts of manufactured products based on historical data. Machine health condition prediction of critical components under the situation of insufficient data, missing prior fault knowledge, and noisy measurement are studied using an enhanced online sequential learning-fuzzy neural network. Meanwhile, Weibull model-based reliability analysis is investigated in this chapter. Performance of various Weibull parameter estimation methods is compared using case studies. Results of this part of research have enabled the development of a product reliability analysis tool that is able to characterize the product failure modes, failure rate, and reliability profile.
Similar content being viewed by others
References
Abernethy RB (2006) The new Weibull handbook, 5th edn. Robert B Abernethy, Florida
Artana KB, Ishida K (2002) Spreadsheet modeling of optimal maintenance schedule for components in wear-out phase. Reliab Eng Syst Saf 77(1):81–91
Brown M, Harris C (1994) Neuro-fuzzy adaptive modeling and control. Prentice Hall, Upper Saddle River
Calixto E (2013) Gas and oil reliability engineering, Elsevier Inc. Oxford
Chen CC, Vachtsevanos G (2012) Bearing condition prediction considering uncertainty: an interval type-2 fuzzy neural network approach. Robot Comput Integr Manuf 28(4):509–516
Chen CC, Zhang B, Vachtsevanos G et al (2011) Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Trans Ind Electron 58(9):4353–4364
Chen CC, Zhang B, Vachtsevanos G (2012) Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms. IEEE Trans Instrum Meas 61(2):297–306
Fitzgibbon K, Barker R, Clayton T, Wilson N (2002) A failure-forecast method based on Weibull and statistical-pattern analysis, reliability and maintainability symposium (IEEE), Seattle, USA. pp 516–521
Groer PG (2000) Analysis of time-to-failure with a Weibull model. In: Proceedings of the maintenance and reliability conference, MARCON 2000, Knoxville
Gu Y, Li J (2012) Engine failure data analysis method based on Weibull distribution model. Appl Mech Mater 128–129:850–854
Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Huynh HT, Won Y (2011) Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks. Pattern Recognit Lett 32(14):1930–1935
Jardine AKS, Lin DM, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Proc 20(7):1483–1510
Li W (2004) Evaluating mean life of power system equipment with limited end-of-life failure data. IEEE Trans Power Syst 19(1):236–242
Liang NY, Huang GB, Saratchandran P et al (2006) A fast and accurate online sequential learning algorithm for feed-forward networks. IEEE Trans Neural Netw 17(6):1411–1423
Lin Y, Cunningham GA III (1995) A new approach to fuzzy-neural system modeling. IEEE Trans Fuzzy Syst 3(2):190–198
Liu J, Wang WS, Golnaraghi F (2009) A multi-step predictor with a variable input pattern for system state forecasting. Mech Syst Signal Proc 23(5):1586–1599
Lourenco RBR, Mello DAA (2012) On the exponential assumption for the time-to-repair in optical network availability analysis. In: Proceedings of 14th international conference on transparent optical networks (ICTON), Coventry, UK. 2–5 July 2012, pp 1–4
Mazhar MI, Kara S, Kaebernick H (2007) Remaining life estimation of used components in consumer products: life cycle data analysis by Weibull and artificial neural networks. J Oper Manag 25(6):1184–1193
Mazhar MI, Salman M, Ian H (2010) Assessing the reliability of system modules used in multiple life cycles. In: Proceedings of the 4th World Congress on Engineering asset management. Athens, Greece, 29–30 Sep 2009, pp 567–573
Nectoux P, Gouriveau R, Medjaher K, et al. (2012) PRONOSTIA: an experimental platform for bearings accelerated degradation tests. In: IEEE international conference prognostics health management, Denver, pp 1–8
Pasha GR, Shuaib Khan M, Pasha AH (2006) Empirical analysis of the Weibull distribution for failure data. J Stat 13(1):33–45
Pham H (2006) Springer handbook of engineering statistics, 1st edn. Springer, London
Porotsky S (2012) Remaining useful life estimation for systems with non-trendability behaviour. In: IEEE international conference prognostics health management, Denver, pp 1–6
Rong HJ, Huang GB, Sundararajan N et al (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man Cybern B Cybern 39(4):1067–1072
Si XS, Wang W, Hu CH et al (2011) Remaining useful life estimation – a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14
Tian ZG, Zuo MJ (2010) Health condition prediction of gears using a recurrent neural network approach. IEEE Trans Reliab 59(4):700–705
Vachtsevanos GJ, Lewis FL, Roemer M et al (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, Hoboken
Wang W (2007) An adaptive predictor for dynamic system forecasting. Mech Syst Signal Proc 21:809–823
Wang W, Golnaraghi MF, Ismail F (2004) Prognosis of machine health condition using neuro-fuzzy systems. Mech Syst Signal Proc 18(4):813–831
Weibull W (1951) A statistical distribution function of wide applicability. J Appl Mech Trans ASME 18(3):293–297
Wu SQ, Er MJ, Gao Y (2001) A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks. IEEE Trans Fuzzy Syst 9(4):578–594
Yu M, Wang D, Luo M, Huang L (2011) Prognosis of hybrid systems with multiple incipient faults: augmented global analytical redundancy relations approach. IEEE Trans Syst Man Cybern A 41(3):540–551
Yu M, Wang D, Luo M, Chen Q (2012) Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns. Expert Syst Appl 39(11):9955–9965
Zhao FG, Chen J, Guo L et al (2009) Neuro-fuzzy based condition prediction of bearing health. J Vib Control 15(7):1079–1091
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag London
About this entry
Cite this entry
Li, X., Lu, W.F., Zhai, L., Er, M.J., Pan, Y. (2014). Predictive Modeling for Life Cycle Reliability Analysis and Machine Health Condition Prediction in Remanufacturing. In: Nee, A. (eds) Handbook of Manufacturing Engineering and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-4976-7_57-1
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4976-7_57-1
Received:
Accepted:
Published:
Publisher Name: Springer, London
Online ISBN: 978-1-4471-4976-7
eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering