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首页> 外文会议>World Congress on Intelligent Control and Automation >Intelligent fault diagnosis of plunger pump in truck crane based on a hybrid fault diagnosis scheme
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Intelligent fault diagnosis of plunger pump in truck crane based on a hybrid fault diagnosis scheme

机译：基于混合故障诊断方案的汽车起重机柱塞泵智能故障诊断

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At the initial stage of the mechanism, the collected samples are always in actual state, and the signals in fault conditions are gathered after a certain running time, so the general fault diagnosis model cannot be trained effectively. In this paper, a hybrid fault diagnosis scheme for pump in truck crane was proposed based on particle swarm optimization (PSO) SVDD and DBI K-Cluster method. Firstly, the SVDD procedure was constructed with the data in actual state, and the model parameters were optimized with PSO algorithm. Secondly, when the total number of novelty samples reached a given threshold, the K-Cluster method was utilized to classify the collected samples and the labels were allocated. In this procedure, the number of the class was determined with the Davies Bouldin index (DBI). Finally, each class data was trained with SVDD, and a whole diagnosis model was constructed with all the two-class classifiers. For the multi-fault mode samples of the pump in truck crane, experiments show that a promising classification performance is achieved.
机译：在该机制的初始阶段，所收集的样本始终处于实际状态，并且在运行一定时间后会收集故障条件下的信号，因此无法有效地训练通用故障诊断模型。提出了一种基于粒子群算法（SSOVDD）和DBI K-Cluster方法的卡车起重机混合故障诊断方案。首先，以实际状态的数据构造SVDD程序，并使用PSO算法对模型参数进行优化。其次，当新奇样本总数达到给定阈值时，使用K-Cluster方法对收集的样本进行分类，并分配标签。在此过程中，类别的数量由Davies Bouldin指数（DBI）确定。最后，使用SVDD对每个分类数据进行训练，并使用所有两个分类器构建了一个完整的诊断模型。对于汽车起重机中泵的多故障模式样本，实验表明，该系统具有良好的分类性能。

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