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AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ

Year 2020, , 53 - 61, 15.04.2020
https://doi.org/10.31796/ogummf.619239

Abstract

Dijital dönüşüm sanayideki
birçok sürecin veri odaklı yeni yaklaşımlarla ele alınmasını gerekli
kılmaktadır. Bu bağlamda Endüstri 4.0 ile beraber akıllı fabrikalarda da önemli
dijital dönüşümün olması beklenmektedir. Akıllı fabrikalardaki dijital dönüşüme
katkı sağlayacak en önemli teknolojilerden bir tanesi de otonom taşıyıcı araç
(OTA)’lardır.  OTA’ların fabrika
içerisindeki görevlerini verimli bir şeklide gerçekleştirmeleri ve beklenmedik
bir problem veya aksama olduğunda insan müdahalesi olmadan bu durumun veri
üzerinden tespiti önemlidir. Bu çalışmada, Bulanık mantık ile OTA’ların fabrika
içerisindeki trafik ağında oluşabilecek beklenmedik durma, yavaşlama vb.
kaynaklı anormal durumlar tespit edilmektedir. Yapılan testlerde önerilen
yöntemin %84,62 başarıyla sonuç verdiği gözlenmiştir.

Supporting Institution

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’nun (TÜBİTAK)

Project Number

116E731

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.
  • Nikolaev, A. B., Sapego, Y. S., Jakubovich, A. N., Berner, L. I., & Stroganov, V. Y. (2016). Fuzzy Algorithm for the Detection of Incidents in the Transport System. International Journal of Environmental and Science Education, 11(16), 9039-9059.
  • ÖRNEK, Ö, VATAN, S, SARIOĞLU, S, ve YAZICI, A. (2018). Trafik Ağlarında Anomali Tespiti. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 26 (3), 132-138. DOI: 10.31796/ogummf.440285.
  • Örnek, Ö., Vatan, S., Sarıoğlu, S., ve Yazıcı, A. (2018). Anomaly Detection for Autonomous Transfer Vehicles in Smart Factories. CEIT 2018 6th International Conference on Control Engineering and Information Technology 25-27 October, IEEE, pp. 756-760.
  • Parkany, E., & Xie, C. (2005). A complete review of incident detection algorithms & their deployment: what works and what doesn't (No. NETCR 37, NETC 00-7).
  • Rossi, R., Gastaldi, M., Gecchele, G., & Barbaro, V. (2015). Fuzzy logic-based incident detection system using loop detectors data. Transportation Research Procedia, 10, 266-275.
  • Thaika, M., Tasneeyapant, S., & Cheamanunkul, S. (2018). A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection. 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), 1-6. 10.1109/JCSSE.2018.8457338.
  • Weil, R., Garcia-Ortiz, A., & Wootton, J. (1998, May). Detection of traffic anomalies using fuzzy logic based techniques. In 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228) (Vol. 2, pp. 1176-1181).
  • Zadeh, L. A. (1988, April). Fuzzy logic. In Computer, vol. 21, no. 4, pp. 83-93. IEEE. doi: 10.1109/2.53

FUZZY LOGIC BASED ANOMALY DETECTION FOR AUTONOMOUS TRANSPORT VEHICLES IN SMART FACTORIES

Year 2020, , 53 - 61, 15.04.2020
https://doi.org/10.31796/ogummf.619239

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%).  

Project Number

116E731

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.
  • Nikolaev, A. B., Sapego, Y. S., Jakubovich, A. N., Berner, L. I., & Stroganov, V. Y. (2016). Fuzzy Algorithm for the Detection of Incidents in the Transport System. International Journal of Environmental and Science Education, 11(16), 9039-9059.
  • ÖRNEK, Ö, VATAN, S, SARIOĞLU, S, ve YAZICI, A. (2018). Trafik Ağlarında Anomali Tespiti. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi, 26 (3), 132-138. DOI: 10.31796/ogummf.440285.
  • Örnek, Ö., Vatan, S., Sarıoğlu, S., ve Yazıcı, A. (2018). Anomaly Detection for Autonomous Transfer Vehicles in Smart Factories. CEIT 2018 6th International Conference on Control Engineering and Information Technology 25-27 October, IEEE, pp. 756-760.
  • Parkany, E., & Xie, C. (2005). A complete review of incident detection algorithms & their deployment: what works and what doesn't (No. NETCR 37, NETC 00-7).
  • Rossi, R., Gastaldi, M., Gecchele, G., & Barbaro, V. (2015). Fuzzy logic-based incident detection system using loop detectors data. Transportation Research Procedia, 10, 266-275.
  • Thaika, M., Tasneeyapant, S., & Cheamanunkul, S. (2018). A Fast, Scalable, Unsupervised Approach to Real-time Traffic Incident Detection. 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), 1-6. 10.1109/JCSSE.2018.8457338.
  • Weil, R., Garcia-Ortiz, A., & Wootton, J. (1998, May). Detection of traffic anomalies using fuzzy logic based techniques. In 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228) (Vol. 2, pp. 1176-1181).
  • Zadeh, L. A. (1988, April). Fuzzy logic. In Computer, vol. 21, no. 4, pp. 83-93. IEEE. doi: 10.1109/2.53
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Özlem Örnek 0000-0002-8775-8695

Eyyüp Gülbandılar 0000-0001-5559-5281

Ahmet Yazıcı 0000-0001-5589-2032

Project Number 116E731
Publication Date April 15, 2020
Acceptance Date March 3, 2020
Published in Issue Year 2020

Cite

APA Örnek, Ö., Gülbandılar, E., & Yazıcı, A. (2020). AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 28(1), 53-61. https://doi.org/10.31796/ogummf.619239
AMA Örnek Ö, Gülbandılar E, Yazıcı A. AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ. ESOGÜ Müh Mim Fak Derg. April 2020;28(1):53-61. doi:10.31796/ogummf.619239
Chicago Örnek, Özlem, Eyyüp Gülbandılar, and Ahmet Yazıcı. “AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 28, no. 1 (April 2020): 53-61. https://doi.org/10.31796/ogummf.619239.
EndNote Örnek Ö, Gülbandılar E, Yazıcı A (April 1, 2020) AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28 1 53–61.
IEEE Ö. Örnek, E. Gülbandılar, and A. Yazıcı, “AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ”, ESOGÜ Müh Mim Fak Derg, vol. 28, no. 1, pp. 53–61, 2020, doi: 10.31796/ogummf.619239.
ISNAD Örnek, Özlem et al. “AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 28/1 (April 2020), 53-61. https://doi.org/10.31796/ogummf.619239.
JAMA Örnek Ö, Gülbandılar E, Yazıcı A. AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ. ESOGÜ Müh Mim Fak Derg. 2020;28:53–61.
MLA Örnek, Özlem et al. “AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 28, no. 1, 2020, pp. 53-61, doi:10.31796/ogummf.619239.
Vancouver Örnek Ö, Gülbandılar E, Yazıcı A. AKILLI FABRİKALARDAKİ OTONOM TAŞIYICILAR İÇİN BULANIK MANTIK TABANLI ANOMALİ TESPİTİ. ESOGÜ Müh Mim Fak Derg. 2020;28(1):53-61.

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