A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic response...A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.展开更多
在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving ...在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving Average Model),在常规的自然环境模态分析算法的基础上构造出一种快速求解NEx T-ARMA模型的算法进行桥梁模态识别。相比于传统的环境激励模态参数计算方法,该算法不但降低了传统算法的复杂度,而且采用了反馈的方式提高了计算精度。采用ANSYS建立有限元模型并搭建简易实验系统分别对该算法进行仿真验证和实验验证,验证结果表明,该算法能够有效地在自然激励下提取出桥梁结构的各阶模态,其中对前三阶固有频率的识别相对误差降到1%左右。展开更多
针对模态辨识结果对输入的敏感性,研究了测量信息对飞行器工作模态辨识精度的影响。介绍了自回归-滑动平均(auto-regressive and moving average,简称ARMA)模型环境激励模态辨识方法的理论、试验测点和激励情况,并给出了试验研究方案情...针对模态辨识结果对输入的敏感性,研究了测量信息对飞行器工作模态辨识精度的影响。介绍了自回归-滑动平均(auto-regressive and moving average,简称ARMA)模型环境激励模态辨识方法的理论、试验测点和激励情况,并给出了试验研究方案情况。通过选择不同测点布置组合,研究了测点布置对辨识结果的影响。对各测点数据人为增加噪声,研究了数据品质对辨识结果的影响。研究发现,测点数目较多,且测点布置在振型数值较大位置,辨识结果较好。展开更多
基金Supported by the National Natural Science Foundation of China（51079027）
文摘A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.
文摘在桥梁建造和维护过程中,需要对桥梁的振动模态进行在线、实时的分析,急需一种不需要人工激励进行快速模态分析的算法。通过研究自然激励技术NEx T(Natural Excitation Technique)与自回归滑动平均模型ARMA(Auto-Regressive and Moving Average Model),在常规的自然环境模态分析算法的基础上构造出一种快速求解NEx T-ARMA模型的算法进行桥梁模态识别。相比于传统的环境激励模态参数计算方法,该算法不但降低了传统算法的复杂度,而且采用了反馈的方式提高了计算精度。采用ANSYS建立有限元模型并搭建简易实验系统分别对该算法进行仿真验证和实验验证,验证结果表明,该算法能够有效地在自然激励下提取出桥梁结构的各阶模态,其中对前三阶固有频率的识别相对误差降到1%左右。
文摘针对模态辨识结果对输入的敏感性,研究了测量信息对飞行器工作模态辨识精度的影响。介绍了自回归-滑动平均(auto-regressive and moving average,简称ARMA)模型环境激励模态辨识方法的理论、试验测点和激励情况,并给出了试验研究方案情况。通过选择不同测点布置组合,研究了测点布置对辨识结果的影响。对各测点数据人为增加噪声,研究了数据品质对辨识结果的影响。研究发现,测点数目较多,且测点布置在振型数值较大位置,辨识结果较好。
