Technical Brief
Year 2020,
Volume: 28 Issue: 3, 289 - 298, 31.12.2020
Abstract
Bu çalışmada, Veri Odaklı Hata Teşhis Sistemi Geliştirilmesi konusunda, üretimdeki CNC tezgâhları ve rulmanları hedefleyen bir uygulamanın detayları, uygulama çıktıları, analizleri ve sonuçları paylaşılmıştır. Üretim ve bilgisayar teknolojilerinin birleşimi ile verimliliği üst düzeye çıkarmak, yapay zekâ yaklaşımları ile insan hatalarını en aza indirmek ve makine öğrenmesi ile oluşabilecek hataları önceden tahmin edebilme konusunda yapılmış bir çalışmadan ortaya çıkan analizler sunulmuştur. Önerilen ve uygulanan yöntem ışığında aynı özellikte makinelerin izlenmesini sağlamak amacıyla filo tabanlı bir izleme sisteminin kurulması hedeflenmiştir. Yapılan çalışma sonucunda; motor ve rulman arızaları için prognostik yaklaşımları test etmek amacıyla rulman arızalarını içeren deneyler olmak üzere dört farklı veri seti üzerinde önerilen yöntemin testleri yapılmış ve anomali puanları gösterilmiştir.
Supporting Institution
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TUBİTAK)
Project Number
118C252
Thanks
Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu’nun (TUBİTAK) 118C252 nolu projesi tarafından desteklenmiştir.
References
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- Data-Driven Documents, (2020). JavaScript library for manipulating documents based on data. Erişim Adresi: https://d3js.org
- Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K. & Davis, J. (2019). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, (2020), 106585. doi: http://dx.doi.org/10.1016/j.ymssp.2019.106585
- Jammu, N.S. & Kankar, P.K. (2011). A review on prognosis of rolling element bearings, International Journal of Engineering Science and Technology (IJEST)
- Kozlov, A.M., Al-jonid, Kh.M.,Kozlov, A.A. & Antar Sh.D. (217). Product quality management based on CNC machine fault prognostics and diagnosis. IOP Conf. Series: Materials Science and Engineering, 327 (2018), 022067. doi: http://dx.doi.org/10.1088/1757-899X/327/2/022067
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- Saidi, L., Ali, J.B., Bechhoefer, E. & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120, (2017), 1–8. doi: http://dx.doi.org/10.1016/j.apacoust.2017.01.005
- Yılmaz, M. ve Gürel, U. (2019). CNC tezgâhlarından MT Connect verileri ile duruş ve çalışma analizi. ISAS 2019 3rd International Symposium on Innovative Approaches in Scientific Studies. SETSCI Conference Proceedings, 4(1), 21-25, 2019
Year 2020,
Volume: 28 Issue: 3, 289 - 298, 31.12.2020
Abstract
Project Number
118C252
References
- Ahmad, W., Khan, S.A. & Kim, J. (2017). A hybrid prognostics technique for rolling element bearings using adaptive predictive model. IEEE Transactions on Industrial Electronics. doi: http://dx.doi.org/10.1109/TIE.2017.2733487
- Chan, Y.S. & Tou Ng.H. (2008). MAXSIM: A maximum similarity metric for machine translation evaluation. Department of Computer Science National University of Singapore Law Link, Singapore 117590
- Cosme, L.B., D’Angelo, M.F.S.V., Caminhas, W. M., Yin, S. & Palhares, R.M. (2017). A novel fault prognostic approach based on particle filters and differential evolution. Springer Science+Business Media, LLC 2017. doi: http://dx.doi.org/10.1007/s10489-017-1013-1
- Data-Driven Documents, (2020). JavaScript library for manipulating documents based on data. Erişim Adresi: https://d3js.org
- Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K. & Davis, J. (2019). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, (2020), 106585. doi: http://dx.doi.org/10.1016/j.ymssp.2019.106585
- Jammu, N.S. & Kankar, P.K. (2011). A review on prognosis of rolling element bearings, International Journal of Engineering Science and Technology (IJEST)
- Kozlov, A.M., Al-jonid, Kh.M.,Kozlov, A.A. & Antar Sh.D. (217). Product quality management based on CNC machine fault prognostics and diagnosis. IOP Conf. Series: Materials Science and Engineering, 327 (2018), 022067. doi: http://dx.doi.org/10.1088/1757-899X/327/2/022067
- Li, Z., Wang, Y. & Wang, K. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2017. doi: http://dx.doi.org/10.1007/s40436-017-0203-8
- Liao, L. & Lee, J. (2009). Design of a reconfigurable prognostics platform for machine tools. Expert Systems with Applications, 37, (2010), 240–252. doi: http://dx.doi.org/10.1016/j.eswa.2009.05.004
- Lüthe, M. (2020). Calculate similarity — the most relevant metrics in a nutshell. Erişim Adresi: https://towardsdatascience.com/calculate-similarity-the-most-relevant-metrics-in-a-nutshell-9a43564f533e
- Saidi, L., Ali, J.B., Bechhoefer, E. & Benbouzid, M. (2017). Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR. Applied Acoustics, 120, (2017), 1–8. doi: http://dx.doi.org/10.1016/j.apacoust.2017.01.005
- Yılmaz, M. ve Gürel, U. (2019). CNC tezgâhlarından MT Connect verileri ile duruş ve çalışma analizi. ISAS 2019 3rd International Symposium on Innovative Approaches in Scientific Studies. SETSCI Conference Proceedings, 4(1), 21-25, 2019
There are 12 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Articles |
| Authors | |
| Project Number | 118C252 |
| Publication Date | December 31, 2020 |
| Acceptance Date | November 4, 2020 |
| Published in Issue | Year 2020 Volume: 28 Issue: 3 |
