Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex un...Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex unit-powerhouse-dam coupling vibration system increases the difficulties of modal parameter identification. In this study, in view of the difficulties of modal order determination and the noise jamming caused by ambient excitation, along with false mode identification and elimination problems, the ensemble empirical mode decomposition （EEMD） method was used to decrease noise, the singular entropy increment spectrum was used to determine system order, and multiple criteria were used to eliminate false modes. The eigensystem realization algorithm （ERA） and stochastic subspace identification （SSI） method were then used to identify modal parameters. The results show that the relative errors of frequencies in the first four modes were within 10% for the ERA method, while those of SSI were over 10% in the second and third modes. Therefore, the ERA method is more appropriate for identifying the structural modal parameters for this particular powerhouse layout.展开更多
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51321065)the National Natural Science Foundation of China(Grants No.51379140,51209158,and 51379177)
文摘Modal parameter identification is a core issue in health monitoring and damage detection for hydraulic structures. For a roof overflow hydropower station with a bulb tubular unit under ambient excitation, a complex unit-powerhouse-dam coupling vibration system increases the difficulties of modal parameter identification. In this study, in view of the difficulties of modal order determination and the noise jamming caused by ambient excitation, along with false mode identification and elimination problems, the ensemble empirical mode decomposition （EEMD） method was used to decrease noise, the singular entropy increment spectrum was used to determine system order, and multiple criteria were used to eliminate false modes. The eigensystem realization algorithm （ERA） and stochastic subspace identification （SSI） method were then used to identify modal parameters. The results show that the relative errors of frequencies in the first four modes were within 10% for the ERA method, while those of SSI were over 10% in the second and third modes. Therefore, the ERA method is more appropriate for identifying the structural modal parameters for this particular powerhouse layout.
