A Genetic Algorithm-Based Ensemble Model for Equipment Condition Assessment with Class-Imbalanced Dataset

This study proposes a modified heterogeneous ensemble model for the purpose of real-time equipment condition assessment. This model makes it possible to plan desired preventive maintenance activities before an unexpected failure takes place. This study focuses on the class-imbalanced problem in equipment condition assessment research, which has not been discussed before. In reality, equipment will experience multiple conditions (states), most of the time remaining in the normal condition and relatively rarely being in the critical condition, which means that, from the perspective of data modelling, the distribution of samples is highly imbalanced among different classes (conditions). The majority of samples belong to the normal condition, while the minority belong to the critical condition, which poses a great challenge to the classification performance. To address this problem, a genetic algorithm-based ensemble model is presented. Numerical experiments including comparison studies have been conducted. The results show the effectiveness of our proposed model over previous models.