Journal
KNOWLEDGE-BASED SYSTEMS
Volume 241, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2022.108296
Keywords
Intelligent fault diagnosis; Imbalanced data; Imbalanced learning; Cost-sensitive learning; Adaptive cost
Categories
Funding
- National Key R&D Program of China [2019YFB2004302]
- Open Fund Project from Henan Key Laboratory of High-performance Bearings [2020ZCKF06]
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This paper proposes a strategy to calculate sample costs adaptively to address the issue of imbalanced industrial data, demonstrating significant improvement in model performance under different sample sets. Analysis of training losses and relationships between parameters reveals the advantages of the proposed strategy.
The natural distribution of industrial data is imbalanced, which deteriorates the performance of intelligent fault diagnostic models. Although cost-sensitive learning is an effective method for solving the data imbalance problem, it suffers from the difficulty of setting optimal costs. Therefore, this paper proposes a strategy that considers the number distribution of samples, the convergence trend of classes, and the convergence trend of samples to calculate sample costs adaptively. Using costs to weigh the sample losses and applying them to different models and different loss functions, the diagnostic results under different sample sets show that the weighted losses can significantly improve the model's performance when using imbalanced data. By further analysing the training loss of the modified model, the angles between deep features, and the angles between deep features and classification weight vectors, it can be found that the dominance of majority classes in imbalanced data is suppressed in training, which is attributed to the loss of each class being coordinated by the proposed strategy. The comparison with various imbalanced learning methods demonstrates the advantages of the proposed method under conditions of large imbalance ratios and complex tasks.(c) 2022 Elsevier B.V. All rights reserved.
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