Time Series Analysis for Fail Spare Part Prediction: Case of ATM Maintenance

Nachirat Rachburee, Samatachai Jantarat, Wattana Punlumjeak

Abstract


Prediction of failed spare parts is an interesting issue in inventory management. Our work applied predictive analytic to forecast future amount of failed spare parts. This research used maintenance time series data from year 2013 to 2016 to train and test data for a prediction model. In the preprocessing step, we looked into new features based on historical data set. Then, we added the day of week feature into the example of the data set. The day of week feature had an impact to spare parts prediction model. Moving average and windowing methods were used in the preprocessing phase before sending through the prediction model. Artificial neural network and support vector machine for regression were applied to predict the amount of failed spare parts. The experiments demonstrated the average accuracy of failed spare part prediction. The result represented that the support vector machine for regression showed the best accuracy at 88.24%. SVR yielded the highest prediction accuracy at 92.7%.

Keywords


Time Series; Prediction; Spare Part;

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References


A. K. ardine, A. H. Tsang. Maintenance, replacement, and reliability: theory and applications, CRC press. (2013).

M. Ben-Daya, U. Kumar, D. P. Murthy. Introduction to Maintenance Engineering: Modelling, Optimization and Management, John Wiley & Sons. (2016).

G. E. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung. Time series analysis: forecasting and control, John Wiley & Sons. (2015).

R. J. Hyndman, G. Athanasopoulos. Forecasting: principles and practice, OTexts. (2014).

P. K. Bala, Purchase-driven classification for Improved Forecasting in spare parts inventory replenishment. International Journal of Computer Applications, 10(9)(2010) 40-45

V. Vaitkus, , G. Zylius, , R. Maskeliunas. Electrical spare parts demand forecasting. Elektronika ir Elektrotechnika, 20(10)(2014) 7-10.

Oded Maimon, Lior Rokach. Data Mining and Knowledge Discovery Handbook. Second Edition Springer Science Business Media, (2010) 6-133.

M. D. Sulistiyo, R. N. Dayawati. Evolution strategies for weight optimization of Artificial Neural Network in time series prediction. 2013 IEEE International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (ROBIONETICS), Yogyakarta, Indonesia, (2013) 143-147.

M. KutyƂowska. Neural network approach for failure rate prediction. Engineering Failure Analysis, 47(2015) 41-48.

L. Wang, Y. Zeng, T. Chen. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems with Applications, 42(2) (2015) 855-863.

R. Alfred, A genetic-based backpropagation neural network for forecasting in time-series data. 2015 International Conference on Science in Information Technology (ICSITech), Yogyakarta, Indonesia, (2015) 158-163.

L. Yu, Y. Chen. A neural network based method for part demands prediction in auto aftermarket. 2010 IEEE International Conference on Software Engineering and Service Sciences, Beijing, China (2010) 648-651.

N. R. Dzakiyullah, B. Hussin, C. Saleh, A. M. Handani, Comparison Neural Network and Support Vector Machine for Production Quantity Prediction. Advanced Science Letters, 20(10-11)(2014) 2129-2133.

J. Soltani, M. Kalanaki, M. Soltani. Fuzzified Pipes Dataset to Predict Failure Rates by Hybrid SVR-PSO Algorithm. Modern Applied Science, 10(7)(2016) 29-35.

P. F. Pai, K. P. Lin, C. S. Lin, P. T. Chang. Time series forecasting by a seasonal support vector regression model. Expert Systems with Applications, 37(6)(2010) 4261-4265.

Z. Wang, J. Wen, D. Hua. Research on distribution network spare parts demand forecasting and inventory quota. 2014 IEEE PES AsiaPacific Power and Energy Engineering Conference (APPEEC), Kowloon, Hong Kong SAR (2014) 1-6.

A. Shirzad, M. Tabesh, R. Farmani. A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks. KSCE Journal of Civil Engineering, 18(4)(2014) 941-94.


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ISSN: 2180-1843

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