Year 2019, Volume 3, Issue 1, Pages 56 - 66 2019-04-30

PREDICTIVE MAINTENANCE FOR INDUSTRY
ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM

Semih Ötleş [1]

40 143

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.

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.

  • 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.
Primary Language tr
Subjects Engineering
Journal Section Research Article
Authors

Orcid: 0000-0003-4571-8764
Author: Semih Ötleş (Primary Author)
Institution: EGE ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date: April 30, 2019

Bibtex @research article { ij3dptdi519896, journal = {International Journal of 3D Printing Technologies and Digital Industry}, issn = {2602-3350}, address = {Kerim ÇETİNKAYA}, year = {2019}, volume = {3}, pages = {56 - 66}, doi = {}, title = {ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM}, key = {cite}, author = {Ötleş, Semih} }
APA Ötleş, S . (2019). ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM. International Journal of 3D Printing Technologies and Digital Industry, 3 (1), 56-66. Retrieved from http://dergipark.org.tr/ij3dptdi/issue/44951/519896
MLA Ötleş, S . "ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM". International Journal of 3D Printing Technologies and Digital Industry 3 (2019): 56-66 <http://dergipark.org.tr/ij3dptdi/issue/44951/519896>
Chicago Ötleş, S . "ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM". International Journal of 3D Printing Technologies and Digital Industry 3 (2019): 56-66
RIS TY - JOUR T1 - ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM AU - Semih Ötleş Y1 - 2019 PY - 2019 N1 - DO - T2 - International Journal of 3D Printing Technologies and Digital Industry JF - Journal JO - JOR SP - 56 EP - 66 VL - 3 IS - 1 SN - 2602-3350- M3 - UR - Y2 - 2019 ER -
EndNote %0 International Journal of 3D Printing Technologies and Digital Industry ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM %A Semih Ötleş %T ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM %D 2019 %J International Journal of 3D Printing Technologies and Digital Industry %P 2602-3350- %V 3 %N 1 %R %U
ISNAD Ötleş, Semih . "ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM". International Journal of 3D Printing Technologies and Digital Industry 3 / 1 (April 2019): 56-66.
AMA Ötleş S . ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM. IJ3DPTDI. 2019; 3(1): 56-66.
Vancouver Ötleş S . ENDÜSTRİ İÇİN KESTİRİMCİ BAKIM. International Journal of 3D Printing Technologies and Digital Industry. 2019; 3(1): 66-56.