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Journals / Bayburt Üniversitesi Fen Bilimleri Dergisi / 2024 / Cilt: 7 Sayı: 2

An Application on Estimation of Machine Failure Times in Cement Production Process

Çimento Üretim Sürecinde Makine Arıza Sürelerinin Tahmini Üzerine Bir Uygulama

Çağatay Teke(BAYBURT ÜNİVERSİTESİ)0000-0002-6975-8544
Mümtaz İpek(SAKARYA ÜNİVERSİTESİ)0000-0001-9619-2403
Pages
133–140
Publication Date
2024-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It was concluded that the failure times follow Weibull distribution and the LS and MLE methods were used to estimate the parameters of the distribution in order to check the fitness.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims at developing a model that would enable us to predict the failure times of machines in cement manufacturing process.
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Key Findings & Data:In this context, Weibull distribution, Least Squares (LS) method and Maximum Likelihood Estimation (MLE) method are applied to assess failure distributions. 167 machine failure data were used in this study and analyzed with Minitab the software.
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Conclusion & Impact:It was concluded that the failure times follow Weibull distribution and the LS and MLE methods were used to estimate the parameters of the distribution in order to check the fitness.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çimento Üretimi · Makine Arızaları · Weibull Dağılımı

Abstract

This study aims at developing a model that would enable us to predict the failure times of machines in cement manufacturing process. The knowledge of machine failures is very vital in maintenance of machines in order to enhance production and minimize on costs of maintenance. In this context, Weibull distribution, Least Squares (LS) method and Maximum Likelihood Estimation (MLE) method are applied to assess failure distributions. 167 machine failure data were used in this study and analyzed with Minitab the software. It was concluded that the failure times follow Weibull distribution and the LS and MLE methods were used to estimate the parameters of the distribution in order to check the fitness. Thus, applying LS method, it is possible to get β = 2.07 and η = 971.7, while applying MLE method, results obtain as β = 2.17 and η = 966. It was noted that these values are rather close and both methods gave almost the same results.

Özet

Bu çalışma, çimento üretim sürecindeki makinelerin arıza sürelerini tahmin etmemizi sağlayacak bir model geliştirmeyi amaçlamaktadır. Makine arızalarının bilinmesi, üretimi artırmak ve bakım maliyetlerini en aza indirmek için makinelerin bakımında çok önemlidir. Bu bağlamda, arıza dağılımlarını değerlendirmek için Weibull dağılımı, En Küçük Kareler (LS) yöntemi ve Maksimum Olabilirlik Tahmini (MLE) yöntemi uygulanmıştır. Bu çalışmada 167 makine arıza verisi kullanılmış ve Minitab yazılımı ile analiz edilmiştir. Arıza sürelerinin Weibull dağılımını takip ettiği sonucuna varılmış ve uygunluğu kontrol etmek amacıyla dağılımın parametrelerini tahmin etmek için LS ve MLE yöntemleri kullanılmıştır. Böylece LS yöntemi uygulandığında β = 2.07 ve η = 971.7, MLE yöntemi uygulandığında ise β = 2.17 ve η = 966 olarak elde edilmiştir. Bu değerlerin oldukça yakın olduğu ve her iki yöntemin de neredeyse aynı sonuçları verdiği görülmüştür.

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Cite This Article

Teke, Ç., İpek, M. (2024). An Application on Estimation of Machine Failure Times in Cement Production Process. Bayburt Üniversitesi Fen Bilimleri Dergisi, Cilt: 7 Sayı: 2, 133–140. https://doi.org/10.55117/bufbd.1598345

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References (15)

  1. Ç. Teke, “Design and implementation of a method for determining the optimum maintenance policy in manufacturing sector,” Sakarya University, Türkiye, 2018.
  2. S. M. A. Aljeddani and M. A. Mohammed, “A novel approach to Weibull distribution for the assessment of wind energy speed,” Alex. Eng. J., vol. 78, pp. 56–64, 2023.
  3. L. Kamberı̇, S. Orhanı̇, M. Shaqı̇rı̇, and S. Idrı̇zı̇, “Comparison of three-parameter weibull distributionparameter estimators with the maximum likelihood method,” Sak. Univ. J. Sci., 2022.
  4. Ş. Atamer and K. Çavdar, “Reliability analysis of a single stage gearbox,” UUJFE, vol. 14, no. 1, pp. 39–53, 2009.
  5. O. Alkan, R. Ozçelı̇k, and Ş. Kalkanli, “Modeling diameter distribution of oriental beech in the Almus region using the Weibull distribution,” Turk. J. For., vol. 24, no. 3, pp. 197–207, 2023.
  6. B. Yaniktepe and O. Kara, “Estimating wind energy potential using three different statistical distribution methods,” Cukurova University Journal of the Faculty of Engineering, vol. 36, no. 2, pp. 359–368, 2021.
  7. F. Oral, “Statistical analysis of the wind energy potential of Bitlis province,” DUJE, vol. 11, no. 2, pp. 671–678, 2020.
  8. O. Bı̇ngöl and A. Bulut, “Estimation of Weibull distribution parameters for wind energy applications: A case study of Dinar region in Turkey,” IJTS, vol. 14, no. 1, pp. 1–10, 2022.
  9. Z. R. Shu and M. Jesson, “Estimation of Weibull parameters for wind energy analysis across the UK,” J. Renew. Sustain. Energy, vol. 13, no. 2, p. 023303, 2021.
  10. I. Hussain, A. Haider, Z. Ullah, M. Russo, G. M. Casolino, and B. Azeem, “Comparative analysis of eight numerical methods using Weibull distribution to estimate wind power density for coastal areas in Pakistan,” Energies, vol. 16, no. 3, p. 1515, 2023.
  11. M. Yalçınkaya and B. Birgören, “Estimating confidence lower bounds of Weibull lower percentiles with small samples in material reliability analysis,” Pamukkale Univ. J. Eng. Sci., vol. 26, no. 1, pp. 184–194, 2020.
  12. F. Ç. Zeytinoğlu, “Comparison of statistical prediction methods for Weibull distribution scale and shape parameters,” İstanbul Ticaret University Journal of Social Sciences, vol. 8, no. 15, pp. 73–87, 2009.
  13. K. Doğanşahin, A. F. Uslu, and B. Kekezoğlu, “Modeling of wind speed probability distribution with twocomponent Weibull distributions,” European Journal of Science and Technology, no:15, pp. 315–326, 2019.
  14. M. Danacı, B. Birgören, and S. Ersöz, “Estimation algorithms for Weibull parameters and percentiles,” J. Fac. Eng. Arch. Gazi Univ., vol. 24, no. 1, pp. 119–128, 2009.
  15. C. K. Seal and A. H. Sherry, “Weibull distribution of brittle failures in the transition region,” Procedia Struct. Integr., vol. 2, pp. 1668–1675, 2016