An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine

Abstract

In this study, a support vector regression (SVR) model is developed for reliability estimation. An imperialist competitive algorithm is applied for selecting the SVR parameters such as ∁, . The proposed model is validated by applying it to a benchmark data set. Satisfactory performance of the proposed model with respect to the data set is demonstrated through a comparative study. A shearing machine operating at an electric tableau manufacturing company is considered a case study. A set of data representing the time-to-failure (TTF) of the shearing machine is used to calculate the cumulative TTF for reliability modelling. The experimental results indicate that the proposed model achieves high estimation accuracy.

Keywords:

• support vector regression (SVR)
• imperialist competitive algorithm (ICA)
• system reliability
• shearing machine

Acknowledgement

The authors are grateful for the valuable comments and suggestions from the respected reviewers. Their valuable comments and suggestions have enhanced the strength and significance of our paper.

Additional information

Funding

The authors are grateful for the support provided by the College of Engineering, University of Tehran, Iran. This study was supported by a grant from the University of Tehran [grant number 8106013/1/19]. This study was also supported by a grant from Iran National Science Foundation [grant number 93010029].