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<p>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.</p>

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Advanced stacking models for machine fault diagnosis with ensemble trees and SVM

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Yuhua Liao,
  • Ming Li,
  • Qingshuai Sun,
  • Pude Li

摘要

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.