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Structure Fault Diagnosis of Tower Crane Based on Wavelet Packet Analysis and Support Vector Machines

机译：基于小波包分析和支持向量机的塔式起重机结构故障诊断

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First establish a dynamic model of tower crane in the load lifting process, the lifting load is solved under two work conditions. Then establish the FEM(finite element analysis) model of the tower crane under the normal and the damage condition. Get the dynamic displacement of the normal and the damage status under the lifting dynamic load. With wavelet packet decomposition and SVM(Support vector machines) multi-classification algorithm, a multi-fault classifier is constructed, and applied to the fault diagnosis of tower body. The results of the study show that the multi-fault classifier has such advantages as simple algorithm and excellent capability of fault classification, and it can not only diagnose the structural damage status, but also determine the positions of structural damage. This will be a new search on tower crane structural health diagnosis.
机译：首先在负载提升过程中建立塔式起重机动态模型，在两个工作条件下解决了升降载荷。然后在正常和损伤条件下建立塔式起重机的FEM（有限元分析）模型。在提升动态负载下获得正常的动态位移和损坏状态。利用小波包分解和SVM（支持向量机）多分类算法，构建了多故障分类器，并应用于塔体的故障诊断。研究结果表明，多故障分类器具有简单算法和优异的故障分类能力，不仅可以诊断结构损坏状态，还可以确定结构损坏的位置。这将是塔式起重机结构健康诊断的新搜索。

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