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<p>Expanders in organic Rankine cycle systems serve as critical energy-conversion components in low-grade waste heat recovery installations, yet their reliable operation is threatened by faults such as bearing defects, rotor imbalance, and blade cracking. Conventional diagnostic methods often struggle with non-stationary vibration characteristics, class imbalance, and low signal-to-noise ratios inherent to these working environments. This paper proposes an improved deep residual network, referred to as multi-scale convolutional block attention module residual network, that integrates a multi-scale parallel feature extraction module with convolutional block attention mechanisms for intelligent fault diagnosis. The multi-scale module employs three parallel convolutional branches with different kernel sizes to simultaneously capture transient impulses, periodic modulation, and low-frequency envelope features across multiple temporal scales. Attention-enhanced residual blocks sequentially recalibrate channel and spatial responses to emphasize fault-sensitive features while suppressing noise interference. A training optimization scheme combining Focal Loss, cosine annealing, and targeted data augmentation is further introduced to address the small-sample imbalanced-data challenge. Five-fold cross-validation experiments conducted on a 10&#xa0;kW single-screw expander test rig demonstrate that the proposed model achieves 98.11 ± 0.34% diagnostic accuracy across four health states, surpassing the standard deep residual network baseline by 6.57 percentage points, with only 3.27% relative accuracy degradation at 10&#xa0;dB signal-to-noise ratio. Ablation studies confirm a multiplicative synergy between the multi-scale and attention modules, statistical significance tests validate the robustness of the observed improvements, and comparative evaluations against six benchmark methods demonstrate the superiority and generalizability of the proposed approach.</p>

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Fault diagnosis of ORC expanders using a multiscale residual network with convolutional block attention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Jing Li,
  • Zexu Gao

摘要

Expanders in organic Rankine cycle systems serve as critical energy-conversion components in low-grade waste heat recovery installations, yet their reliable operation is threatened by faults such as bearing defects, rotor imbalance, and blade cracking. Conventional diagnostic methods often struggle with non-stationary vibration characteristics, class imbalance, and low signal-to-noise ratios inherent to these working environments. This paper proposes an improved deep residual network, referred to as multi-scale convolutional block attention module residual network, that integrates a multi-scale parallel feature extraction module with convolutional block attention mechanisms for intelligent fault diagnosis. The multi-scale module employs three parallel convolutional branches with different kernel sizes to simultaneously capture transient impulses, periodic modulation, and low-frequency envelope features across multiple temporal scales. Attention-enhanced residual blocks sequentially recalibrate channel and spatial responses to emphasize fault-sensitive features while suppressing noise interference. A training optimization scheme combining Focal Loss, cosine annealing, and targeted data augmentation is further introduced to address the small-sample imbalanced-data challenge. Five-fold cross-validation experiments conducted on a 10 kW single-screw expander test rig demonstrate that the proposed model achieves 98.11 ± 0.34% diagnostic accuracy across four health states, surpassing the standard deep residual network baseline by 6.57 percentage points, with only 3.27% relative accuracy degradation at 10 dB signal-to-noise ratio. Ablation studies confirm a multiplicative synergy between the multi-scale and attention modules, statistical significance tests validate the robustness of the observed improvements, and comparative evaluations against six benchmark methods demonstrate the superiority and generalizability of the proposed approach.