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.