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A Novel Ensemble-Learning-Based Convolution Neural Network for Handling Imbalanced Data

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Abstract

Deep-learning-based fault diagnosis of wind turbine has played a significant role in advancing the renewable energy industry. However, the imbalanced data sampled by the supervisory control and data acquisition systems has led to low diagnosis accuracy. Additionally, deep neural networks can encounter issues like gradient vanishing and insufficient feature learning during backpropagation when the model is too deep. This article introduces a novel approach that is based on dynamic weight loss functions to modulate unbalanced data and improve diagnostic accuracy by focusing on misclassification of a small sample number. The proposed approach employs a 1D-CNN model and an ensemble-learning-based convolution neural network (EL-CNN) to enhance diversity of models and complementarity of feature learning. The EL-CNN model addresses the problem of local features being overlooked and provides more accurate results. The effectiveness of this proposed approach is well demonstrated through experimental cases on real wind turbine pitch system fault data. Two different networks using three different loss functions and three state-of-the-art fault diagnosis models are tested, demonstrating the EL-CNN model’s superiority.

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Data Availability

The data that support the findings of this study are available from the corresponding author, C. Wen, upon reasonable request.

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Funding

This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 820776 (INTEGRADDE), the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, the Alexander von Humboldt Foundation of Germany, the BRIEF Award of Brunel University London, the National Natural Science Foundation of China under Grant 61973209, the Natural Science Foundation of Shanghai of China under Grant 20ZR1421200, and the Capacity Building Project of Shanghai Local Colleges and Universities of China under Grant 22010501100.

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Authors and Affiliations

  1. School of Electrical Engineering, Shanghai Dianji University, Shanghai, 201306, China

    Xianbin Wu & Chuanbo Wen

  2. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang & Weibo Liu

  3. School of Electronic Information Engineering, Shanghai Dianji University, Shanghai, 201306, China

    Junjie Yang

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  1. Xianbin Wu
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  2. Chuanbo Wen
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Correspondence to Chuanbo Wen.

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Wu, X., Wen, C., Wang, Z. et al. A Novel Ensemble-Learning-Based Convolution Neural Network for Handling Imbalanced Data. Cogn Comput 16, 177–190 (2024). https://doi.org/10.1007/s12559-023-10187-8

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  • DOI: https://doi.org/10.1007/s12559-023-10187-8

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