Nonlinear auto-regressive model identification and its application in structural damage detection
Ma Jiaxin1, Xu Feiyun1, Huang Kai2, Huang Ren1
1. School of Mechanical Engineering, Southeast University, Nanjing 211189, China;
2. Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210036, China
Abstract:The similarities between the general expression for the linear and nonlinear auto-regressive model with exogenous inputs (GNARX) and Volterra series model, and the internal links between GNARX and auto-regressive model with exogenous inputs (ARX) are analyzed. On the basis of the structure characteristics of GNARX model, a structure pruning algorithm based on parameters’ rate of standard deviation is proposed and applied to model structure identification for GNARX model. With data simulation, the feasibility and effectiveness of the method is verified. Finally, GNARX model together with the proposed structure identification method is applied to structural damage detection for steel plate. The results show that GNARX model, whose structure is identified with structure pruning algorithm based on parameters’ rate of standard deviation, has the highest identification accuracy of structural damage, which indicates the superiority of GNARX model and its structure pruning algorithm applied to structural damage detection.
马家欣1,许飞云1,黄凯2,黄仁1. 非线性自回归模型辨识及其在结构损伤识别中的应用[J]. 振动与冲击, 2017, 36(20): 118-124.
Ma Jiaxin1, Xu Feiyun1, Huang Kai2, Huang Ren1. Nonlinear auto-regressive model identification and its application in structural damage detection. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(20): 118-124.
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