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<p>Status monitoring and fault diagnosis of mechanical equipment are vital for ensuring operational safety. However, real-world diagnostic scenarios often suffer from limited and imbalanced fault data, affecting model accuracy and reliability. This study addresses these challenges by focusing on bearings and hydraulic pumps as research objects. A dual attention-deep convolutional generative adversarial network (DA-DCGAN) is proposed to generate fault signals and enhance diagnosis under imbalanced conditions.Initially, fault vibration signals are converted into time-frequency maps using continuous wavelet transform (CWT) to highlight key features. These maps are used to train the DA-DCGAN, which generates additional fault samples to augment the imbalanced dataset. The expanded dataset is then used to train two classifiers, CNN and DA-CNN, to evaluate their ability to capture minority class fault features. Experimental evaluations on bearing and hydraulic pump datasets reveal that the proposed approach significantly improves classification performance across varying imbalance ratios.The results demonstrate that DA-DCGAN effectively enhances diagnostic accuracy and model generalization under imbalanced sample conditions, offering a robust solution for fault diagnosis in mechanical systems.</p>

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Fault diagnosis methods for imbalanced samples of hydraulic pumps based on DA-DCGAN

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Yang Zhao,
  • Anqi Jiang,
  • Wanlu Jiang,
  • Peiyao Zhang,
  • Peng Xiong

摘要

Status monitoring and fault diagnosis of mechanical equipment are vital for ensuring operational safety. However, real-world diagnostic scenarios often suffer from limited and imbalanced fault data, affecting model accuracy and reliability. This study addresses these challenges by focusing on bearings and hydraulic pumps as research objects. A dual attention-deep convolutional generative adversarial network (DA-DCGAN) is proposed to generate fault signals and enhance diagnosis under imbalanced conditions.Initially, fault vibration signals are converted into time-frequency maps using continuous wavelet transform (CWT) to highlight key features. These maps are used to train the DA-DCGAN, which generates additional fault samples to augment the imbalanced dataset. The expanded dataset is then used to train two classifiers, CNN and DA-CNN, to evaluate their ability to capture minority class fault features. Experimental evaluations on bearing and hydraulic pump datasets reveal that the proposed approach significantly improves classification performance across varying imbalance ratios.The results demonstrate that DA-DCGAN effectively enhances diagnostic accuracy and model generalization under imbalanced sample conditions, offering a robust solution for fault diagnosis in mechanical systems.