基于全局注意力残差收缩网络的柱塞泵故障诊断方法

Fault diagnosis of piston pump based on global attention residual shrinkage network

  • 摘要:
    目的 针对传统神经网络在强噪声干扰下特征提取能力不足的问题,提出一种新的全局注意力残差收缩网络,实现复杂环境下柱塞泵故障精准诊断。
    方法 首先,对原始监测信号进行数据切分;建立一种新的带有注意力机制的全局特征提取器,从监测信号中提取故障相关特征,同时引入阈值软化机制,减少信号中噪声干扰的影响;然后,对网络模型进行反向传播优化,减少损失误差,提升模型的诊断性能;最后,将特征提取结果输入到故障分类器进行故障识别。基于柱塞泵故障模拟实验台,验证所提出方法的有效性。
    结果 结果表明:相比其他模型,所建立的全局注意力残差收缩网络模型有更高的诊断精度,且具备更强的抗干扰能力。
    结论 该诊断方法能够在复杂恶劣环境下实现故障的精准诊断。

     

    Abstract:
    Objectives Aiming at the problem of insufficient feature extraction ability of traditional neural network under strong noise interference, a new global attention residual shrinkage network is proposed to realize the accurate diagnosis of piston pump faults under the complex external environment.
    Methods Firstly, data slicing is performed on the original signals; then, a new global feature extractor with an attention mechanism is established to extract fault-related features from the monitoring signals, while a threshold softening mechanism is introduced to reduce the influence of noise interference in the signals; then, back propagation optimization is performed on the network model to reduce the loss and improve the diagnostic performance of the model. Finally, the feature extraction results are input into the fault classifier for fault identification. The effectiveness of the proposed method is verified based on a piston pump fault simulation test bed.
    Results The results show that compared with other models, the established global attention residual shrinkage network model has higher diagnostic accuracy and stronger anti-interference ability.
    Conclusions It is shown that the proposed global attention residual shrinkage network-based fault diagnosis method for piston pumps is able to realize accurate fault diagnosis in complex and harsh environments.

     

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