RESEARCH PAPER
Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning
1
The State Key Lab of Mechanical Transmission Chongqing University Chongqing, China
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):68-80
KEYWORDS
ABSTRACT
This study proposes an ensemble learning model for the purpose of performing a 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. 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
learning model is presented. Furthermore, a self-updating learning strategy is presented for online monitoring, contributing to
adaptability and reliability enhancement along with time. Many previous studies have attempted feature extraction and to set
thresholds for equipment health indicators. This study has an advantage of omitting these steps, as it can directly assess the
equipment condition through the proposed ensemble learning model. Numerical experiments, including two types of comparison
studies, have been conducted. The results show the greater effectiveness of our proposed model over that of previous research in
terms of the stability and accuracy of its classification performance
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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