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<p>Due to aging, high operational loads, and harsh working environments, industrial robots are prone to various failures. Predictive maintenance (PdM) offers a promising solution to this challenge. This study proposes a PdM method for industrial robots based on AM-CNN-BiGRU neural network. First, a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) are integrated to leverage the CNN’s ability to extract local spatial features and the BiGRU’s capability for learning global bidirectional temporal dependencies. This combination effectively captures the nonlinear and deep features within the data. Second, vibration, current, and torque data from the robot joints are input into the CNN as distinct channels, enabling multi-source data fusion. Concurrently, an attention mechanism (AM) is incorporated to assign adaptive weights to different data sources, enhancing the identification of critical fault signatures. Finally, ablation studies were conducted, and comparative experiments were performed between AM-CNN-BiGRU and several of the most representative and latest algorithms in the time series forecasting domain. During training, the Lion optimizer was employed for the deep learning models. The results demonstrate that AM-CNN-BiGRU achieved optimal performance across four evaluation metrics: R², MAE, MAPE, and RMSE, thereby validating its effectiveness and reliability.</p>

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An AM-CNN-BiGRU network with spatiotemporal feature fusion for industrial robot predictive maintenance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Zihao Zang,
  • Yiwen Zhang,
  • Shouxin Ruan,
  • Lei Wang,
  • Xinming Zhang,
  • Linsen Song,
  • Yibo Huang,
  • Shuhong Ye

摘要

Due to aging, high operational loads, and harsh working environments, industrial robots are prone to various failures. Predictive maintenance (PdM) offers a promising solution to this challenge. This study proposes a PdM method for industrial robots based on AM-CNN-BiGRU neural network. First, a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) are integrated to leverage the CNN’s ability to extract local spatial features and the BiGRU’s capability for learning global bidirectional temporal dependencies. This combination effectively captures the nonlinear and deep features within the data. Second, vibration, current, and torque data from the robot joints are input into the CNN as distinct channels, enabling multi-source data fusion. Concurrently, an attention mechanism (AM) is incorporated to assign adaptive weights to different data sources, enhancing the identification of critical fault signatures. Finally, ablation studies were conducted, and comparative experiments were performed between AM-CNN-BiGRU and several of the most representative and latest algorithms in the time series forecasting domain. During training, the Lion optimizer was employed for the deep learning models. The results demonstrate that AM-CNN-BiGRU achieved optimal performance across four evaluation metrics: R², MAE, MAPE, and RMSE, thereby validating its effectiveness and reliability.