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Control chart pattern recognition with variable window size for imbalanced data based on convolutional neural network with a convolutional block attention module
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DOI：10.1016/j.neucom.2025.131226.png)
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Abstract
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• The proposed MFF-CNN-CBAM model effectively handles up to 12 CCPR problems. • The proposed MFF-CNN-CBAM addresses multi-class classification for imbalanced data. • Variable windows mechanism enhances adaptability in dynamic environments. • Proposed method outperforms traditional and deep learning approaches. • Online monitoring system enables real-time pattern recognition in dynamic data.
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