학술논문
Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance.
Document Type
Article
Author
Source
Subject
*FEATURE selection
*GENERATIVE adversarial networks
*PREDICTION models
*STATISTICS
*SCARCITY
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Language
ISSN
2045-2322
Abstract
Predictive maintenance harnesses statistical analysis to preemptively identify equipment and system faults, facilitating cost- effective preventive measures. Machine learning algorithms enable comprehensive analysis of historical data, revealing emerging patterns and accurate predictions of impending system failures. Common hurdles in applying ML algorithms to PdM include data scarcity, data imbalance due to few failure instances, and the temporal dependence nature of PdM data. This study proposes an ML-based approach that adapts to these hurdles through the generation of synthetic data, temporal feature extraction, and the creation of failure horizons. The approach employs Generative Adversarial Networks to generate synthetic data and LSTM layers to extract temporal features. ML algorithms trained on the generated data achieved high accuracies: ANN (88.98%), Random Forest (74.15%), Decision Tree (73.82%), KNN (74.02%), and XGBoost (73.93%). [ABSTRACT FROM AUTHOR]