1. Pandya, M., R. N. Patel, and S. K. P. Amarnath, Determination of Time Delay and Rate of Temperature Change during Tyre Curing (Vulcanizing) Cycle. Procedia Engineering, 2013. 51: p. 828- 833.
2. Wang, Y., B. Su, and J. Wu, Simulation and optimization of giant radial tire vulcanization process. Procedia Engineering, 2012. 31: p. 723- 726.
3. Ghoreishy, M. H. R. and G. Naderi, Three dimensional finite element modelling of truck tyre curing process in mould. Iranian Polymer Journal, 2005. 14(8): p. 735- 743.
4. Guo, J. and H. Yang, A fault detection method for heat loss in a tyre vulcanization workshop using a dynamic energy consumption model and predictive baselines. Applied Thermal Engineering, 2015. 90: p. 711- 721.
5. Browne, A. L. and L. Wickliffe, Rubber emissivity and the thermal state of tires. Tire Science and Technology, 1979. 7(3): p. 71-89.
6. Schlanger, H., A one-dimensional numerical model of heat transfer in the process of tire vulcanization. Rubber Chemistry and Technology, 1983. 56(2): p. 304- 321.
7. Karaağaç, B., M. İnal, and V. Deniz, Predicting optimum cure time of rubber compounds by means of ANFIS. Materials & Design, 2012. 35: p. 833- 838.
8. Jokinen, J. and J. L. M. Lastra. Implementation of nonintrusive monitoring and fault diagnosis in industrial robot system. in 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). 2016.
9. Lopez- Perez, D. and J. Antonino-Daviu, Application of infrared thermography to failure detection in industrial induction motors: case stories. IEEE Transactions on Industry Applications, 2017. PP(99): p. 1- 1.
10. Abdelgayed, T., W. Morsi, and T. Sidhu, A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit. IEEE Transactions on Smart Grid, 2017. PP(99): p. 1- 1.
11. Ray, P., et al. Fault classification of a long transmission line using nearest neighbor algorithm and boolean indicators. in 2016 International Conference on Next Generation Intelligent Systems (ICNGIS). 2016.
12. Yu, H., S. Yin, and Y. Yunqiang. A data driven fault detection scheme design for nonlinear industrial systems. in IECON 2016- 42nd Annual Conference of the IEEE Industrial Electronics Society. 2016.
13. Isermann, R., Model-based fault-detection and diagnosis–status and applications. Annual Reviews in control, 2005. 29(1): p. 71- 85.
14. Castillo, I. and T. Edgar. Model Based Fault Detection and Diagnosis. in TWCCC Conference. 2008.
15. Isermann, R., Fault-diagnosis systems: an introduction from fault detection to fault tolerance. 2006: Springer Science & Business Media.
16. Jain, A.K., R. P. W. Duin, and M. Jianchang, Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000. 22(1): p. 4- 37.
17. Xia, F., et al. Research of condenser fault diagnosis method based on neural network and information fusion. in 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE). 2010.
18. Werbos, P.J., Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 1990. 78(10): p. 1550-1560.
19. Oliveira- Santos, T., et al. Submersible Motor Pump Fault Diagnosis System: A Comparative Study of Classification Methods. in 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). 2016.
20. Rajeswari, C., et al., A Gear Fault Identification using Wavelet Transform, Rough set Based GA, ANN and C4.5 Algorithm. Procedia Engineering, 2014. 97: p. 1831- 1841.
21. Budiman, F. N. and E.S. Wahyuni. Discrimination of particle-initiated defects in gas-insulated system using C4.5 algorithm. in 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). 2016.
22. Yasenjiang, J., et al. Fault mode prediction based on decision tree. in 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). 2016.
23. Lin, Y., et al. A method of satellite network fault synthetic diagnosis based on C4.5 algorithm and expert knowledge database. in 2015 International Conference on Wireless Communications & Signal Processing (WCSP). 2015.
24. Li, B., et al. A Scenario- Based Approach to Predicting Software Defects Using Compressed C4.5 Model. in 2014 IEEE 38th Annual Computer Software and Applications Conference. 2014.