A comparative analysis of maximum likelihood estimation and artificial neural network modeling to assess electrical component reliability

Çolak A. B., Sindhu T. N., Lone S. A., Akhtar M. T., Shafiq A.

Quality and Reliability Engineering International, cilt.40, sa.1, ss.91-114, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1002/qre.3233
  • Dergi Adı: Quality and Reliability Engineering International
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.91-114
  • Anahtar Kelimeler: mean time to failure, reliability function, maximum likelihood estimation, artificial neural network, failure rate function
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

This study focuses on accurately predicting the behavior of new power function distribution using neural network and optimizing it using maximum likelihood estimation. The main motivation of this study is that there is no study in the literature that optimizes and predicts the reliability analysis of lifetime models by combining artificial neural networks and maximum likelihood estimation methods. The numerical findings of the reliability investigations and the values got from maximum likelihood estimation and artificial neural network modeling have been examined and investigated carefully. For the artificial neural network models, the R value was 0.99999 and the deviation ratios were lower than 0.08%. The findings reveal that artificial neural networks are a powerful and useful mathematical tool for analyzing the reliability of lifetime models and numerical study findings via maximum likelihood estimation are completely in accord with artificial neural network prediction results.