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Time Series Prediction Techniques for Estimating Remaining Useful Lifetime of Cutting Tool Failure


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DOI: https://doi.org/10.15866/irecos.v9i10.3004

Abstract


Prognostic have progressed over in the last few years as a specific function. It provides remaining useful lifetime (RUL) estimation of the targeted equipment or component in which able to be beneficially used by production or maintenance people to be readily advanced through preventive maintenance actions. In order to get accurate RUL for predicting future failure, RUL estimation is depending on the current condition of equipment. However, existing prognostic works use historical run-to-failure data and simulation-based model which is difficult to predict the future failure occurrence from the current certain level of degradation equipment.  Therefore, this paper reported the use of time series prediction techniques in estimating RUL from established degradation index. Artificial Neural Network (ANN) with time series and Double Exponential Smoothing (DES) approaches with some modification is used to carry out the prediction steps. The modification obtained two variants of multi-step time series name predictions namely hybrid ANN-DES and Enhanced Double Exponential Smoothing (EDES). All the techniques are compared and evaluated to investigate the performance accuracy based on RMSE. The results shows that the EDES has a better solution in RUL estimation compare than other techniques.
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Keywords


Time Series Prediction; Artificial Neural Network; Double Exponential Smoothing; Remaining Useful Lifetime; Prognostics

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References


V. T. Tran, and B. S. Yang, “Data-driven approach to machine condition prognosis using least square regression tree,” Journal of mechanical science and technology, vol. 23, no. 5, pp. 1468-1475, 2009.
http://dx.doi.org/10.1007/s12206-008-1218-7

D. Djurdjanovic, J. Lee, and J. Ni, “Watchdog Agent--an infotronics-based prognostics approach for product performance degradation assessment and prediction,” Advanced Engineering Informatics, vol. 17, no. 3-4, pp. 109-125, 2003.
http://dx.doi.org/10.1016/j.aei.2004.07.005

V. Tran, B. Yang, M. Oh et al., “Machine condition prognosis based on regression trees and one-step-ahead prediction,” Mechanical Systems and Signal Processing, vol. 22, no. 5, pp. 1179-1193, 2008.
http://dx.doi.org/10.1016/j.ymssp.2007.11.012

A. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483-1510, 2006.
http://dx.doi.org/10.1016/j.ymssp.2005.09.012

J. Luo, M. Namburu, K. Pattipati et al., "Model-based prognostic techniques [maintenance applications]." pp. 330-340.
http://dx.doi.org/10.1109/autest.2003.1243596

J. Luo, K. Pattipati, L. Qiao et al., “Model-based prognostic techniques applied to a suspension system,” IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 38, no. 5, pp. 1156-1168, 2008.
http://dx.doi.org/10.1109/tsmca.2008.2001055

A. Muller, M. Suhner, and B. Iung, “Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system,” Reliability Engineering and System Safety, vol. 93, no. 2, pp. 234-253, 2008.
http://dx.doi.org/10.1016/j.ress.2006.12.004

N. Gebraeel, A. Elwany, and J. Pan, “Residual life predictions in the absence of prior degradation knowledge,” Reliability, IEEE Transactions on, vol. 58, no. 1, pp. 106-117, 2009.
http://dx.doi.org/10.1109/tr.2008.2011659

K. Medjaher, and N. Zerhouni, “Residual-based failure prognostic in dynamic systems,” SAFE PROCESS, no. sur CD ROM, 2009.

A. H. Elwany, and N. Z. Gebraeel, “Sensor-driven prognostic models for equipment replacement and spare parts inventory,” IIE Transactions, vol. 40, no. 7, pp. 629-639, 2008.
http://dx.doi.org/10.1080/07408170701730818

M. Schwabacher, "A survey of data-driven prognostics."
http://dx.doi.org/10.2514/6.2005-7002

L. Mann, A. Saxena, and G. M. Knapp, “Statistical-based or condition-based preventive maintenance?,” Journal of Quality in Maintenance Engineering, vol. 1, no. 1, pp. 46-59, 1995.
http://dx.doi.org/10.1108/13552519510083156

Y. Wang, J. Qiao, and L. Ma, “Framework Construction of Condition-Based Maintenance System,” Applied Mechanics and Materials, vol. 34, pp. 1195-1199, 2010.
http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1195

W. Wang, and B. Hussin, “Plant residual time modelling based on observed variables in oil samples,” Journal of the Operational Research Society, vol. 60, no. 6, pp. 789-796, 2008.
http://dx.doi.org/10.1057/palgrave.jors.2602621

S. A. Asmai, A. S. H. Basari, A. S. Shibghatullah et al., "Neural network prognostics model for industrial equipment maintenance." pp. 635-640.
http://dx.doi.org/10.1109/his.2011.6122180

J. Lee, J. Ni, D. Djurdjanovic et al., “Intelligent prognostics tools and e-maintenance,” Computers in industry, vol. 57, no. 6, pp. 476-489, 2006.
http://dx.doi.org/10.1016/j.compind.2006.02.014

J. Yan, M. Koc, and J. Lee, “A prognostic algorithm for machine performance assessment and its application,” Production Planning & Control, vol. 15, no. 8, pp. 796-801, 2004.
http://dx.doi.org/10.1080/09537280412331309208

W. Caesarendra, A. Widodo, and B. Yang, “Application of relevance vector machine and logistic regression for machine degradation assessment,” Mechanical Systems and Signal Processing, vol. 24, no. 4, pp. 1161-1171, 2010.
http://dx.doi.org/10.1016/j.ymssp.2009.10.011

E. Kumar, and S. Chaturvedi, “True degradation estimation of industrial equipment with fuzzy sets: a case study,” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, vol. 223, no. 2, pp. 167-179, 2009.
http://dx.doi.org/10.1243/1748006xjrr201

H.-E. Kim, A. C. Tan, J. Mathew et al., “Bearing fault prognosis based on health state probability estimation,” Expert Systems with Applicaions, vol. 39, no. 5, pp. 5200-5213, 2012.
http://dx.doi.org/10.1016/j.eswa.2011.11.019

W. Caesarendra, A. Widodo, and B. Yang, “Application of relevance vector machine and logistic regression for machine degradation assessment,” Mechanical Systems and Signal Processing, 2009.
http://dx.doi.org/10.1016/j.ymssp.2009.10.011

L. Liao, and J. Lee, “Design of a reconfigurable prognostics platform for machine tools,” Expert Systems with Applications, vol. 37, no. 1, pp. 240-252, 2010.
http://dx.doi.org/10.1016/j.eswa.2009.05.004

Y. Zhao, “On preventive maintenance policy of a critical reliability level for system subject to degradation,” Reliability Engineering & System Safety, vol. 79, no. 3, pp. 301-308, 2003.
http://dx.doi.org/10.1016/s0951-8320(02)00201-6

C. S. Byington, M. J. Roemer, and T. Galie, "Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]." pp. 6-2815-6-2824 vol. 6.
http://dx.doi.org/10.1109/aero.2002.1036120

S. Agatonovic-Kustrin, and R. Beresford, “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research,” Journal of pharmaceutical and biomedical analysis, vol. 22, no. 5, pp. 717-727, 2000.
http://dx.doi.org/10.1016/s0731-7085(99)00272-1

Zahran, B.M., Classification of brain tumor using neural network, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 673-678.

Ashour, M.W., Khalid, F., Abdullah, L.N., Halin, A.A., Artificial neural network-based texture classification using reduced multidirectional Gabor features, (2014) International Review on Computers and Software (IRECOS), 9 (6), pp. 1007-1016.

Mercy Geraldine, J., Kirubakaran, E., An efficient technique for frequent item set mining in time series data with aid of AFCM, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2765-2772.

Sumathi, N., Sathiyabama, S., Mining of cyclic periodic patterns for prediction system, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 310-318.

Y. Sun, V. Babovic, and E. Chan, “Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory,” Journal of Hydrology, 2010.
http://dx.doi.org/10.1016/j.jhydrol.2010.10.020

Krishneswari, K., Arumugam, S., An Enhanced Backpropagation Neural Network for Multimodal Biometric System, (2014) International Review on Computers and Software (IRECOS), 9 (7), pp. 1208-1213.

Asmai, S.A.; Abdullah, R.W.; Soh, M.N.C.; Basari, A.S.H.; Hussin, B., "Application of multi-step time series prediction for industrial equipment prognostic," Open Systems (ICOS), 2011 IEEE Conference on , vol., no., pp.273,277, 25-28 Sept. 2011.
http://dx.doi.org/10.1109/icos.2011.6079285

C. T. Cheng, J. X. Xie, K. W. Chau et al., “A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction,” Journal of Hydrology, vol. 361, no. 1, pp. 118-130, 2008.
http://dx.doi.org/10.1016/j.jhydrol.2008.07.040

J. X. Xie, C. T. Cheng, K. W. Chau et al., “A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity,” International Journal of Environment and Pollution, vol. 28, no. 3, pp. 364-381, 2006.
http://dx.doi.org/10.1504/ijep.2006.011217

M. Khashei, and M. Bijari, “An artificial neural network (p, d, q) model for timeseries forecasting,” Expert Systems with Applications, vol. 37, no. 1, pp. 479-489, 2010.
http://dx.doi.org/10.1016/j.eswa.2009.05.044

Lee, J. et al (2004). Infotronics technologies and predictive tools for next-generation maintenance systems. In: Proc. 11th Symposium on Information Control Problems in Manufacturing, Elsevier Ltd.

G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003.
http://dx.doi.org/10.1016/s0925-2312(01)00702-0


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