Abstract
Fault diagnosis plays an integral role in machine health monitoring. However, in practical applications, there are obvious differences in class distribution within the data, leading to poor performance of the algorithm in identifying a few classes. Meanwhile, overfitting and computational resource requirements have become a challenge. Recently, the stacking model has been promoted in the field of fault diagnosis, but its performance evaluation of stacking models in many literature is not comprehensive enough. In this paper, an Advanced Ensemble Trees model (AET) is proposed. The SMOTE (Synthetic Minority Oversampling Technique) resampling technique is used to optimise the dataset balance. Then, the advantages of Support Vector Machines (SVM) and multi-tree models are combined to form a robust base model using hyper-parameter tuning. Simple Logistic Regression (LR) is used as a meta-model to construct the new stacking model. Through extensive experimental validation, it is found that the AET model is close to 99% in several key performance metrics and outperforms existing machine learning methods and relatively short model training time.
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The UCI AI4I 2020 Predictive Maintenance Dataset is available at: https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset. The CWRU Dataset is available at: https://engineering.case.edu/bearingdatacenter/download-data-file.
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Acknowledgements
This research was funded by Hunan Provincial Regional Joint Fund Project (Grant No. 2024JJ7179).
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Yuhua Liao, conceptualization methodology, experiment, validation and writing draft manuscript. Ming Li, conceptualization, methodology, formal analysis. Qingshuai Sun, methodology, validation. Pude Li, supervision, validation.
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Liao, Y., Li, M., Sun, Q. et al. Advanced stacking models for machine fault diagnosis with ensemble trees and SVM. Appl Intell 55, 251 (2025). https://doi.org/10.1007/s10489-024-06206-2
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DOI: https://doi.org/10.1007/s10489-024-06206-2