Journal
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
Volume 21, Issue 1, Pages 68-80Publisher
POLISH MAINTENANCE SOC
DOI: 10.17531/ein.2019.1.9
Keywords
condition assessment; heterogeneous ensemble learning; genetic algorithm; class-imbalanced
Categories
Funding
- National Science Foundation of China [51035008]
- National Science & Technology Major Project of China [2016ZX04004-005]
- Fundamental Research Funds for the State Key Laboratory of Mechanical Transmission of Chongqing University [SKLMT-ZZKT-2017M16]
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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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