Time Series Analysis for Fail Spare Part Prediction: Case of ATM Maintenance
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. Kutyłowska. 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.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.