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Kritik Üretim Sistemlerinde Kenar Bilişimin Önemi: FPGA Uygulaması

Yıl 2022, Sayı: 43, 41 - 47, 30.11.2022
https://doi.org/10.31590/ejosat.1201855

Öz

Endüstri 4.0 ile birlikte ivme kazanmakla birlikte hayatımızın her alanına girmiş olan akıllı sistemler ile ilgili akademik ve endüstriyel çalışmalar hızla devam etmektedir. Özellikle üretimde kullanılan kalıp, baskı, şekillendirme, kesme vb. gibi farklı makineler, belli bir otomasyona sahiptirler. Bunların işledikleri malzeme bilgileri ve ortak çalışan diğer makinelerle uyumu bütün üretim sisteminin etkin şekilde çalışabilmesi çok önemlidir. Elde edilen verilerin bolluğu daha iyi analitik inceleme için önemliyken, bu verilerden iş amaçlı bilgi elde etmek ve karar destek sistemlerine yardımcı olmak organizasyondaki Bilgi Teknolojileri ve Sistemlerinden beklenen en önemli görevdir. Bu çalışmada üretim ortamındaki cihazlardan, sensörlerden ve işletmedeki diğer Bilgi Sistemlerinden gelen verilerin FPGA tabanlı bir kenar bilişim altyapısı ile değerlendirilmesi önerilmektedir. Bu çalışma kapsamında üretim ortamında kullanılan örnek bir plastik enjeksiyon kalıp cihazına ait bakım kararının tahminlemesi yapılmıştır. Bunun için Internet üzerinden erişilen temsili hız, titreşim ve sıcaklık gibi faktörleri içeren bir veri seti kullanılmış, sensör verileri ve mevcut bilgi sistemi girişleri (ERP, MES) ile gerekli ML algoritmalarını çalıştıracak bir FPGA (Alan Programlanabilir Ağ Dizisi) tasarımı da, Xilinx Design Tools ve Vitis IDE 2020.2 kullanılarak gerçekleştirilmiştir.Veri girişi için üzerinde akıllı algoritmaların çalıştırılacağı FPGA donanımı “Xilinx Zynq xc7z020” kenar bilişim altyapısının esas öğesi olarak planlanmıştır. Çalışmada hibrit bir yaklaşım olan ANFIS (Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi) sistemi Yapay Zekâ uygulaması olarak seçilmiştir. Elde edilen tahmin sonuçları literatürdeki çalışmalarda erişilen doğruluk oranları üzerinden değerlendirilmiştir.

Kaynakça

  • Akhtari S., et al. (2019). Intelligent Embedded Load Detection at the Edge on Industry 4.0 Powertrains Applications, IEEE 5th International forum on Research and Technology for Society and Industry (RTSI).
  • Ali M. Abdulshahed, Andrew, P. Longstaff, Simon Fletcher. (2015). The application of ANFIS prediction models for thermal error compensation on CNC machine tools, Applied Soft Computing, 27, pp.158-168.
  • Andreas, S. Selim, E. and Sihn, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP, 52, pp.161-166.
  • Axenie, C., Bortoli, S. (2020). Predictive Maintenance Dataset. Available: https://zenodo.org/record/ 3653909#. X_nBA OgzZPY
  • Banner, Fault Detection. Available: https://www. bannerengineering.com/tr/tr/solutions/error-proofing.html? pageNum= 1&#all
  • Crosser, Factory Floor Integration in Industry 4.0. Available:https://www.crosser.io/blog/posts/2020/ January/ factory-floor-integration-in-industry-40-complementing- the-isa-95-automation-pyramid/
  • De Blasi S., Engels E. (2020). Next generation control units simplifying industrial machine learning, IEEE 29th International Symposium on Industrial Electronics (ISIE).
  • Ercan, T. 2005. Modeling and Designing Wireless Networks for Corporations: Security Policies and Reconfiguration. Dokuz Eylul University, Graduate School of Natural and Applied Sciences, Ph.D. Thesis.
  • Ercan, T., Al Azzawi, AK.,(2019). Design of an FPGA-based Intelligent Gateway for Industrial IoT. International Journal of Advanced Trends in Computer Science and Engineering, 8, (1.2), pp.126-130.
  • Feng X. et al. (2019). Accelerating CNN-RNN Based Machine Health Monitoring on FPGA, IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS).
  • FPGA Example. Available: http://jjmk.dk/MMMI/ PLDs/FPGA/ fpga.h11.jpg.
  • Gougam F. et al. (2020). Health Monitoring Approach of Bearing: of Adaptive Neuro-Fuzzy Inference System (ANFIS) for RUL-estimation and Autogram Analysis for Fault-localization, Prognostics and Health Management Conference (PHM).
  • Hofmann, E. and Rüsch, M. (2017). Industry 4.0 and the current status as well as prospects on logistics, Computers in Industry, 89, pp.23-34.
  • Hushmat A.K., Kar, G.M., Rather, A.(2019). Survey on Edge-Based Internet-of-Things, International Journal of Computer Networks and Applications (IJCNA), 6, 6.
  • IoT application areas. Available: https://iot-analytics.com/top-10-iot-project-applications
  • Jian, Q., Ying L. and Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond, Procedia CIRP, 52, pp.173-178.
  • Kalaycı, İ., Ercan, T. (2018). Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method, 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara-Turkey, pp.1-6.
  • Kar S, Das S, Ghosh P.K.. (2014). Applications of neuro-fuzzy systems: a brief review and future outline. Appl Soft Comput, 15, pp.243–259.
  • Karaboga, D., Kaya, E. (2019). Adaptive network-based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev, 52, pp.2263–2293.
  • Khazal A., Ercan T. (2018). ANFIS Analysis of Wireless Sensor Data with FPGA. ACTA INFOLOGICA, 2(1), pp.22-32.
  • Lu, Y.,(2017). Industry 4.0: A Survey on Technologies, Applications and Open Research Issues, Journal of Industrial Information Integration, 6: pp.1–10.
  • Plachy, J., Becvar, Z. and Strinati, E. C. (2015). Cross-layer approach enabling communication of a high number of devices in 5G mobile networks, Proc. IEEE 11th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob), pp.809-816.
  • Prinz, C., Morlock, F., Freith, S. Kreggenfeld, N. Kreimeier, D. and Kuhlenkötter, B. (2016). Learning Factory Modules for Smart Factories in Industrie 4.0, Procedia CIRP, 54, pp.113-118.
  • Sisman et al. (2018). The importance of PLC in the predictive maintenance of electronic equipment, IEEE 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI).
  • Trappey, A., C. Trappey, U. Govindarajan, A. Chuang, and J. Sun. (2017). A Review of Essential Standards and Patent Landscapes for the Internet of Things: A Key Enabler for Industry 4.0., Advanced Engineering Informatics, 33: pp.208–229.
  • van der Broec, C.H. et al. (2020). Time Monitoring of Thermal Response and Life-Time Varying Parameters in Power Modules, IEEE Transactions on Industry Applications, 56, 5, pp.5279-5291.
  • Yuriyama, M. and Kushida, T. (2010). Sensor-Cloud Infrastructure- Physical SENSOR Management with Virtualized Sensors on Cloud Computing, NBiS, pp.1-8.

Importance of Edge Computing in Critical Manufacturing Systems: FPGA Implementation

Yıl 2022, Sayı: 43, 41 - 47, 30.11.2022
https://doi.org/10.31590/ejosat.1201855

Öz

Academic and industrial studies on smart systems, which have entered all areas of our lives, continue to their rapid developments along with gaining momentum with Industry 4.0. Especially, many of the different production devices with their molding, printing, shaping, and cutting capabilities have a certain level of automation. They work together with the material information they process, and the compatibility with other machines to make the whole production system work effectively and properly. While the abundance of data acquired is an important source for better analytics, obtaining information for business purposes from this data and helping decision support systems is the most important task expected from Information Technology and Systems in the organization. In this paper, we propose an FPGA-based edge information infrastructure to evaluate critical data from the production devices, distributed sensors, and other ISs in any industrial environment to increase the utilization and performance of the total machinery. This study helps the predictive maintenance decision for a sample plastic injection molding device according to our industrial scenario. A sample data set downloaded from the Internet with the factors like speed, vibration, and the temperature was used. An FPGA (Field Programmable Gate Array) design that will run the necessary ML algorithms with the sensor data and existing information system inputs (ERP, MES) has been carried out by using Xilinx Design Tools and Vitis IDE 2020.2. In this study, the ANFIS (Adaptive Network-Based Fuzzy Inference System) system, which is an approach consisting of the integration of artificial neural networks and Fuzzy Logic, has been chosen as an Artificial Intelligence application. The estimation results obtained were evaluated over the accuracy rates achieved in similar studies in the literature.

Kaynakça

  • Akhtari S., et al. (2019). Intelligent Embedded Load Detection at the Edge on Industry 4.0 Powertrains Applications, IEEE 5th International forum on Research and Technology for Society and Industry (RTSI).
  • Ali M. Abdulshahed, Andrew, P. Longstaff, Simon Fletcher. (2015). The application of ANFIS prediction models for thermal error compensation on CNC machine tools, Applied Soft Computing, 27, pp.158-168.
  • Andreas, S. Selim, E. and Sihn, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP, 52, pp.161-166.
  • Axenie, C., Bortoli, S. (2020). Predictive Maintenance Dataset. Available: https://zenodo.org/record/ 3653909#. X_nBA OgzZPY
  • Banner, Fault Detection. Available: https://www. bannerengineering.com/tr/tr/solutions/error-proofing.html? pageNum= 1&#all
  • Crosser, Factory Floor Integration in Industry 4.0. Available:https://www.crosser.io/blog/posts/2020/ January/ factory-floor-integration-in-industry-40-complementing- the-isa-95-automation-pyramid/
  • De Blasi S., Engels E. (2020). Next generation control units simplifying industrial machine learning, IEEE 29th International Symposium on Industrial Electronics (ISIE).
  • Ercan, T. 2005. Modeling and Designing Wireless Networks for Corporations: Security Policies and Reconfiguration. Dokuz Eylul University, Graduate School of Natural and Applied Sciences, Ph.D. Thesis.
  • Ercan, T., Al Azzawi, AK.,(2019). Design of an FPGA-based Intelligent Gateway for Industrial IoT. International Journal of Advanced Trends in Computer Science and Engineering, 8, (1.2), pp.126-130.
  • Feng X. et al. (2019). Accelerating CNN-RNN Based Machine Health Monitoring on FPGA, IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS).
  • FPGA Example. Available: http://jjmk.dk/MMMI/ PLDs/FPGA/ fpga.h11.jpg.
  • Gougam F. et al. (2020). Health Monitoring Approach of Bearing: of Adaptive Neuro-Fuzzy Inference System (ANFIS) for RUL-estimation and Autogram Analysis for Fault-localization, Prognostics and Health Management Conference (PHM).
  • Hofmann, E. and Rüsch, M. (2017). Industry 4.0 and the current status as well as prospects on logistics, Computers in Industry, 89, pp.23-34.
  • Hushmat A.K., Kar, G.M., Rather, A.(2019). Survey on Edge-Based Internet-of-Things, International Journal of Computer Networks and Applications (IJCNA), 6, 6.
  • IoT application areas. Available: https://iot-analytics.com/top-10-iot-project-applications
  • Jian, Q., Ying L. and Grosvenor, R. (2016). A categorical framework of manufacturing for industry 4.0 and beyond, Procedia CIRP, 52, pp.173-178.
  • Kalaycı, İ., Ercan, T. (2018). Anomaly Detection in Wireless Sensor Networks Data by Using Histogram Based Outlier Score Method, 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara-Turkey, pp.1-6.
  • Kar S, Das S, Ghosh P.K.. (2014). Applications of neuro-fuzzy systems: a brief review and future outline. Appl Soft Comput, 15, pp.243–259.
  • Karaboga, D., Kaya, E. (2019). Adaptive network-based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev, 52, pp.2263–2293.
  • Khazal A., Ercan T. (2018). ANFIS Analysis of Wireless Sensor Data with FPGA. ACTA INFOLOGICA, 2(1), pp.22-32.
  • Lu, Y.,(2017). Industry 4.0: A Survey on Technologies, Applications and Open Research Issues, Journal of Industrial Information Integration, 6: pp.1–10.
  • Plachy, J., Becvar, Z. and Strinati, E. C. (2015). Cross-layer approach enabling communication of a high number of devices in 5G mobile networks, Proc. IEEE 11th Int. Conf. Wireless Mobile Comput., Netw. Commun. (WiMob), pp.809-816.
  • Prinz, C., Morlock, F., Freith, S. Kreggenfeld, N. Kreimeier, D. and Kuhlenkötter, B. (2016). Learning Factory Modules for Smart Factories in Industrie 4.0, Procedia CIRP, 54, pp.113-118.
  • Sisman et al. (2018). The importance of PLC in the predictive maintenance of electronic equipment, IEEE 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI).
  • Trappey, A., C. Trappey, U. Govindarajan, A. Chuang, and J. Sun. (2017). A Review of Essential Standards and Patent Landscapes for the Internet of Things: A Key Enabler for Industry 4.0., Advanced Engineering Informatics, 33: pp.208–229.
  • van der Broec, C.H. et al. (2020). Time Monitoring of Thermal Response and Life-Time Varying Parameters in Power Modules, IEEE Transactions on Industry Applications, 56, 5, pp.5279-5291.
  • Yuriyama, M. and Kushida, T. (2010). Sensor-Cloud Infrastructure- Physical SENSOR Management with Virtualized Sensors on Cloud Computing, NBiS, pp.1-8.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Tuncay Ercan 0000-0003-0014-5106

Erken Görünüm Tarihi 25 Kasım 2022
Yayımlanma Tarihi 30 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 43

Kaynak Göster

APA Ercan, T. (2022). Importance of Edge Computing in Critical Manufacturing Systems: FPGA Implementation. Avrupa Bilim Ve Teknoloji Dergisi(43), 41-47. https://doi.org/10.31590/ejosat.1201855