FUZZY LOGIC BASED ANOMALY DETECTION FOR AUTONOMOUS TRANSPORT VEHICLES IN SMART FACTORIES
Abstract
Digital transformation requires new data-oriented approaches in industry. In this context, it is expected that there will be significant digital transformation in the smart factories with Industry 4.0. One of the most important technologies that will contribute to digital transformation in smart factories is the autonomous transport vehicle (ATV). ATVs are expected to perform their tasks in the factory in an efficient manner. And it is also expected to detect an unexpected problem or any failure via the data without human intervention. This study aimed determining abnormal conditions of traffic network such as unexpected stop and deceleration by using fuzzy logic in the factory. The performed tests show that the proposed method results success (84.62%).
Keywords
Anomaly Detection,Fuzzy Logic,Smart Factories,Autonomous Vehicle
References
- Chandola, V., Banerjee, A., & Kumar, V. (2007). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 15.
- Dardor, M., Chlyah, M., & Boumhidi, J. (2018, April). Incident detection in signalized urban roads based on genetic algorithm and support vector machine. In 2018 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1-6). IEEE.
- Dogru, N., & Subasi, A. (2018, February). Traffic accident detection using random forest classifier. In 2018 15th Learning and Technology Conference (L&T) (pp. 40-45). IEEE.
- Ki, Y. K., Heo, N. W., Choi, J. W., Ahn, G. H., & Park, K. S. (2018, January). An incident detection algorithm using artificial neural networks and traffic information. In 2018 Cybernetics & Informatics (K&I) (pp. 1-5). IEEE.
- La-inchua, J., Chivapreecha, S., & Thajchayapong, S. (2013, May). A new system for traffic incident detection using fuzzy logic and majority voting. In 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (pp. 1-5). IEEE.
- Li, Y., Guo, T., Xia, R., & Xie, W. (2018). Road traffic anomaly detection based on fuzzy theory. IEEE Access, 6, 40281-40288.
- Liu, Q., Lu, J., Chen, S., & Zhao, K. (2014). Multiple Naïve bayes classifiers ensemble for traffic incident detection. Mathematical Problems in Engineering, 2014.
- Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
- Mendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345-377.
- Nikolaev, A. B., & Sapego, Y. S. (2016). IMPLEMENTATION OF INCIDENT DETECTION ALGORITHM BASED ON FUZZY LOGIC IN PTV VISSIM. International Journal of Advanced Studies, 6(4), 37-45.