The invention discloses a turnout switch machine fault diagnosis method and device based on ensemble learning. The method comprises the following steps: collecting action current signals of a turnout switch machine; calculating a time domain feature and a multi-scale permutation entropy feature of each phase of current signal of the switch machine, and constructing a feature set of each state; calculating correlation factors between the feature sets to obtain the weight of each category feature, and adaptively selecting sensitive features according to a dynamic threshold to divide a training set and a test set; constructing a turnout switch machine fault diagnosis IFL-LightGBM model, and enabling the model to be more concentrated on difficult-to-classify samples by improving a loss function; and training and optimizing parameters of the IFL-LightGBM model by using the training set, and performing fault diagnosis on data of the turnout switch machine to be diagnosed to obtain a fault classification result of the switch machine. According to the method, the information of the three-phase action current of the turnout switch machine is fused, the redundancy of the fused feature set is reduced, the fault identification precision and efficiency are improved, and intelligent fault diagnosis of the turnout switch machine can be realized.
本发明公开了一种基于集成学习的道岔转辙机故障诊断方法及装置,包括以下步骤:采集道岔转辙机动作电流信号;计算转辙机每相电流信号的时域特征和多尺度排列熵特征,构造每种状态的特征集;通过计算特征集之间的相关性因子得到每个类别特征的权重,并根据动态阈值自适应选择敏感特征划分训练集和测试集;构建道岔转辙机故障诊断IFL‑LightGBM模型,通过改进损失函数使得模型更加集中于难分类样本;利用训练集对IFL‑LightGBM模型的参数进行训练和优化,并对待诊断的道岔转辙机数据进行故障诊断,得到转辙机故障分类结果。本发明融合了道岔转辙机三相动作电流的信息,降低了融合特征集的冗余性,提高了故障识别精度和效率,可以实现道岔转辙机的智能故障诊断。
Turnout switch machine fault diagnosis method and device based on ensemble learning
一种基于集成学习的道岔转辙机故障诊断方法及装置
17.10.2023
Patent
Elektronische Ressource
Chinesisch
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