Abstract
Üretim
ekosisteminin küreselleşmesi ve akıllı fabrikalara duyulan talep, gelişen
endüstri için büyük bir zorluğu beraberinde getirmektedir. Bu durum imalat
sektörünü bir sonraki dönüşüm olan tahmine dayalı üretime zorlamaktadır. Daha
rekabetçi hale gelebilmek, üreticilerin verimliliklerini ve üretkenliklerini
artırmak için gelişmiş Siber-Fiziksel Sistem tabanlı analitik yaklaşımları
benimsemeleri gerekmektedir. Makinalar Nesnelerin İnterneti (IoT) ile
sistematik olarak veri toplamak, değiştirmek ve analiz etmek için ortak bir
topluluk olarak bağlanır ve çalıştırılır. Edinilen büyük veri analitik
yöntemler vasıtasıyla yorumlanarak eski sorunlara yeni bakış açıları getirir ve
tamamen yeni araştırma alanlarına olanak tanır. Şirketleri, IoT ile stratejik
veya operasyonel süreçlerle nasıl karlı bir şekilde bütünleşebilecekleri
konusunda bilgilendiren çok az araştırma ve derleme yapılmıştır. Bu çalışmada,
dördüncü sanayi devrimi aşamasında, endüstriyel Siber-Fiziksel Sistemler
ortamındaki büyük veri vasıtasıyla bakım yönetiminin potansiyellerini ve
eğilimlerini dinamik olarak yönetmek için kestirimci bakım araştırılmıştır.
Özellikle, ekipmanın bozulmaya maruz kaldığı bir ortamda optimal önleyici bakım
politikaları, gerçek zamanlı veri kullanımı ve tahmine dayalı makine öğrenimi
algoritmalarının kullanılması yoluyla analiz edilmiştir.
References
- KAYNAKLAR1. Haller S, Karnouskos S, Schroth C. The Internet of Things in an Enterprise Context. Springer, 2009.
- 2. Meyer S, Ruppen A, Magerkurth C. Internet of Things-aware Process Modeling: Integrating IoT Devices as Business Process Resources. In: Advanced Information Systems Engineering, pp. 84–98, 2013.
- 3. Wang C, Vo HT, Ni P. An IoT Application for Fault Diagnosis and Prediction. IEEE International Conference on Data Science and Data Intensive Systems, 2015.
- 4. Lee J, Kao HA, Yang S. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, vol. 16, pp. 3-8, 2014.
- 5. Li Z, Wang K. Industry 4.0 – Potentials for Predictive Maintenance. International Workshop of Advanced Manufacturing and Automation (IWAMA), 2016.
- 6. Standard E. Maintenance terminology. In: European Committee for Standardization 13306, ed. Brussels, 2001.
- 7. Wang KS, Li Z, Braaten J, Yu Q. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs. Advances in Manufacturing, vol. 3, pp. 97-104, 2015.
- 8. Garcia M, Sanz-Bobi MA, del Pico J. SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox. Computers in Industry, vol. 57, pp. 552-568, 2006.
- 9. Liu J, Djurdjanovic D, Ni J, Casoetto N, Lee J. Similarity based method for manufacturing process performance prediction and diagnosis. Computers in Industry, vol. 58, pp. 558-566, 2007.
- 10. İnce M, Bekiroğlu N, Ayçiçek E. Kestirimci Bakım Teknolojilerinin Araştırılması ve Endüstriyel Bir Motorun Amt Sistemi ile Arıza Analizlerinin Çıkarılması. Yıldız Teknik Üniversitesi Elektrik Mühendisliği Bölümü Elektrik Makinaları Anabilim Dalı, 2017.
- 11. Aljumaili M, Wandt K, Karim R, Tretten P. eMaintenance ontologies for data quality support. Journal of Quality in Maintenance Engineering, vol. 21, pp. 358-374, 2015.
- 12. Baheti R, Gill H. Cyber-physical systems. The impact of control technology, vol. 12, pp. 161-166, 2011.
- 13. Chaves LW, Nochta Z. Breakthrough towards the internet of things. In: Unique Radio Innovation for the 21st Century, ed: Springer, 2011, pp. 25-38.
- 14. Bughin J, Chui M, Manyika J. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, vol. 56, pp. 75-86, 2010.
- 15. Li L, Xinrui L, Xinyu L. Cloud-Based Service Composition Architecture for Internet of Things. Communications in Computer and Information Science, Springer, vol. 312, pp. 559-564, 2012.
- 16. O’Donovan P, Leahy K, Bruton K, O’Sullivan DT. Big data in manufacturing: a systematic mapping study. Journal of Big Data, vol. 2, pp. 1-22, 2015.
- 17. Hermann M, Pentek T, Otto B. Design principles for Industrie 4.0 scenarios: a literature review. Technische Universität Dortmund, Dortmund, 2015.
- 18. Wang K. Intelligent Predictive maintenance (IPdM) system – Industry 4.0 scenario. Editors: K. Wang, Y. Wang, J. O. Strandhagen and T. Yu, Proceedings of Advanced Manufacturing and Automation V, WIT Transaction on Engineering Science, Vol 113, pp. 259-268, 2016.
- 19. Ötleş S, Çolak, UC, Ötleş O. Endüstri için Yapay Zekâ. Plastik Ambalaj Dergisi, syf. 46-50, 2018.
Abstract
The
increasing competition with the globalization of the production ecosystem
increases the demand for intelligent factories in the industry day by day. This
situation is forcing bringing with it a major challenge to the manufacturing sector
to produce the next step. Therefore, manufacturers should increase their
efficiency and productivity to become more competitive. To this end, they need
to adopt advanced analytical approaches. Internet of Things (IoT) is used to
collect and store data systematically and to make sense of this data by
analyzing it. The collected large data can be interpreted by means of
analytical methods to bring new perspectives to the old problems and to allow
for new areas of research. Few researches and compilations have been made to
inform companies about how they can profitably integrate with the IoT through
strategic or operational processes. In this study, predictive maintenance has
been investigated to dynamically manage the potentials and trends of
maintenance management by means of large data generated in the industrial
environment. Predictive maintenance; monitoring of the condition of the
equipment and its components before failure and analyzing the data by
analytical methods, evaluating the life expectancy and evaluating the
possibility of failure and evaluating the data. Thus, it plans and optimizes
maintenance by using preventive maintenance policies, real-time data usage and
predictive machine learning algorithms in environments where equipment will be
exposed to unplanned downtime.
References
- KAYNAKLAR1. Haller S, Karnouskos S, Schroth C. The Internet of Things in an Enterprise Context. Springer, 2009.
- 2. Meyer S, Ruppen A, Magerkurth C. Internet of Things-aware Process Modeling: Integrating IoT Devices as Business Process Resources. In: Advanced Information Systems Engineering, pp. 84–98, 2013.
- 3. Wang C, Vo HT, Ni P. An IoT Application for Fault Diagnosis and Prediction. IEEE International Conference on Data Science and Data Intensive Systems, 2015.
- 4. Lee J, Kao HA, Yang S. Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, vol. 16, pp. 3-8, 2014.
- 5. Li Z, Wang K. Industry 4.0 – Potentials for Predictive Maintenance. International Workshop of Advanced Manufacturing and Automation (IWAMA), 2016.
- 6. Standard E. Maintenance terminology. In: European Committee for Standardization 13306, ed. Brussels, 2001.
- 7. Wang KS, Li Z, Braaten J, Yu Q. Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs. Advances in Manufacturing, vol. 3, pp. 97-104, 2015.
- 8. Garcia M, Sanz-Bobi MA, del Pico J. SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox. Computers in Industry, vol. 57, pp. 552-568, 2006.
- 9. Liu J, Djurdjanovic D, Ni J, Casoetto N, Lee J. Similarity based method for manufacturing process performance prediction and diagnosis. Computers in Industry, vol. 58, pp. 558-566, 2007.
- 10. İnce M, Bekiroğlu N, Ayçiçek E. Kestirimci Bakım Teknolojilerinin Araştırılması ve Endüstriyel Bir Motorun Amt Sistemi ile Arıza Analizlerinin Çıkarılması. Yıldız Teknik Üniversitesi Elektrik Mühendisliği Bölümü Elektrik Makinaları Anabilim Dalı, 2017.
- 11. Aljumaili M, Wandt K, Karim R, Tretten P. eMaintenance ontologies for data quality support. Journal of Quality in Maintenance Engineering, vol. 21, pp. 358-374, 2015.
- 12. Baheti R, Gill H. Cyber-physical systems. The impact of control technology, vol. 12, pp. 161-166, 2011.
- 13. Chaves LW, Nochta Z. Breakthrough towards the internet of things. In: Unique Radio Innovation for the 21st Century, ed: Springer, 2011, pp. 25-38.
- 14. Bughin J, Chui M, Manyika J. Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly, vol. 56, pp. 75-86, 2010.
- 15. Li L, Xinrui L, Xinyu L. Cloud-Based Service Composition Architecture for Internet of Things. Communications in Computer and Information Science, Springer, vol. 312, pp. 559-564, 2012.
- 16. O’Donovan P, Leahy K, Bruton K, O’Sullivan DT. Big data in manufacturing: a systematic mapping study. Journal of Big Data, vol. 2, pp. 1-22, 2015.
- 17. Hermann M, Pentek T, Otto B. Design principles for Industrie 4.0 scenarios: a literature review. Technische Universität Dortmund, Dortmund, 2015.
- 18. Wang K. Intelligent Predictive maintenance (IPdM) system – Industry 4.0 scenario. Editors: K. Wang, Y. Wang, J. O. Strandhagen and T. Yu, Proceedings of Advanced Manufacturing and Automation V, WIT Transaction on Engineering Science, Vol 113, pp. 259-268, 2016.
- 19. Ötleş S, Çolak, UC, Ötleş O. Endüstri için Yapay Zekâ. Plastik Ambalaj Dergisi, syf. 46-50, 2018.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | April 30, 2019 |
| Submission Date | January 30, 2019 |
| Published in Issue | Year 2019 Volume: 3 Issue: 1 |
Cite
International Journal of 3D Printing Technologies and Digital Industry is lisenced under Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı