基于时空特征组合模型的转辙机故障诊断
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U284.92

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教育部产学合作协同育人项目(202101023013);甘肃省自然科学基金(20JR5RA396);


Fault diagnosis of switch machine based on spatiotemporal feature combined model
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    摘要:

    为解决转辙机故障诊断领域中存在的单一特征信息提取不足、单一诊断方法难以避免因方法局限性造成的分类误差,同时其存在一定程度的过拟合,以及泛化能力、鲁棒性不足的问题,提出了一种基于时空特征组合模型的故障诊断方法。首先,在ZYJ7电液转辙机的8种故障模式和正常模式所对应的油压曲线上提取时频域小波系数作为原始数据集,采用核主成分分析(KPCA)和长短期记忆网络(LSTM)提取其空间、时间特征,之后基于add思想构建时空特征集。其次,对卷积神经网络(CNN)、LSTM两分类器关键参数寻优后分别进行故障诊断,得到各个故障类型的概率值和误差系数。最后,利用误差倒数法对两分类器各个故障类型的概率值赋予权重,得到最终输出结果。仿真结果表明:CNN-LSTM组合模型诊断准确率达98.14%,较单一多层感知机(MLP)、CNN、LSTM模型准确率分别提升7.40%、5.55%、1.85%。可见此方法有效提高了转辙机诊断准确率,为集成学习模型在转辙机故障诊断领域的应用提供了一种思路。

    Abstract:

    In order to solve the problem that the single feature information extraction is insufficient in the field of switch machine fault diagnosis, and the single diagnosis method is difficult to avoid the classification error caused by the limitation of the method. At the same time, it has a certain degree of over-fitting, as well as insufficient generalization ability and robustness. A fault diagnosis method based on spatiotemporal feature combination model is proposed. Firstly, the time-frequency domain wavelet coefficients are extracted from the oil pressure curves corresponding to the eight fault modes and normal modes of the ZYJ7 electro-hydraulic switch machine as the original data set. The kernel principal component analysis ( KPCA ) and long short-term memory network ( LSTM ) are used to extract the spatial and temporal features, and then the spatio-temporal feature set is constructed based on the add idea. Secondly, after optimizing the key parameters of convolutional neural network ( CNN ) and LSTM, the fault diagnosis is carried out respectively, and the probability value and error coefficient of each fault type are obtained. Finally, the error reciprocal method is used to assign weights to the probability values of each fault type of the two classifiers, and the final output result is obtained. The simulation results show that the diagnostic accuracy of CNN-LSTM combined model is 98.14 %, which is 7.40 %, 5.55 % and 1.85 % higher than that of single multi-layer perceptron ( MLP ), CNN and LSTM models, respectively. It can be seen that this method effectively improves the accuracy of switch machine diagnosis, and provides an idea for the application of ensemble learning model in the field of switch machine fault diagnosis.

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引用本文

刘琦,李建国. 基于时空特征组合模型的转辙机故障诊断[J]. 科学技术与工程, 2024, 24(13): 5538-5545.
Liu Qi, Li Jianguo. Fault diagnosis of switch machine based on spatiotemporal feature combined model[J]. Science Technology and Engineering,2024,24(13):5538-5545.

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  • 收稿日期:2023-06-25
  • 最后修改日期:2024-04-30
  • 录用日期:2023-10-10
  • 在线发布日期: 2024-05-17
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