Intelligent estimation of failure processes in critical systems using hybrid ANNABC under non-homogeneous Poisson modeling

Abstract

This study introduces an intelligent hybrid estimation framework for modeling the time-dependent failure behavior of repairable systems. The proposed approach embeds the Inverse Weibull Process (IWP) within a Non-Homogeneous Poisson Process (NHPP) structure and compares the performance of the traditional Maximum Likelihood Estimation (MLE) method with a hybrid algorithm that integrates Artificial Neural Networks and the Artificial Bee Colony optimization technique (ANN-ABC). Both simulation experiments and an application to real clinical data from 299 patients with heart failure demonstrate that the ANN–ABC estimator achieves lower Root Mean Squared Error (RMSE) and Bayesian Information Criterion (BIC) values, particularly for moderate to large datasets. These findings highlight the potential of hybrid intelligent methods as robust alternatives to conventional estimation techniques, offer improved precision in modeling failure intensities, and support predictive maintenance as well as intelligent healthcare monitoring systems.

Journal of Statistical Research 2026, Vol. 60, No. 1, pp. 33-50.