Considering the relatively limited system dynamic information that include in ambient data, a small change in model order would lead to a large variation in estimated modal parameters and influence the identification accuracy.
However, the high-quality vibration data collection technology calls for a more accurate modal identification method to improve the accuracy of the results.
Rotation impaired within-modal recognition accuracy in both vision and touch, but not cross-modal recognition accuracy.
Figure 2 shows that object rotation substantially degraded recognition accuracy in the within-modal conditions, but only slightly decreased cross-modal recognition accuracy.
A two-way (within- vs. cross-modal, unrotated vs. rotated) repeated-measures analysis of variance RM-ANOVAA) showed that object rotation significantly reduced recognition accuracy (F1,23 = 30.04, p = <.001) and that overall within-modal recognition accuracy was marginally better than overall cross-modal recognition (F1,23 = 4.23, p = .051).051
For the WT-only based identification method, the important issues related to identification accuracy such as modal separation, end-effect, associated with the parameter selection of wavelet function based on Shannon entropy, are given detailed investigations.
The requirement of using spurious harmonics to represent nonlinear and nonstationary signals is fully eliminated via these two key innovations, thereby increasing the accuracy of the subsequent modal identification.
An improved eigensystem realization algorithm was employed to improve the accuracy and the efficiency of modal identification.
As a consequence, the modal identification used to extract damage sensitive features has to face constricting requirements in terms of signals stationarity and performance accuracy.
A nonlinear modal identification is then performed.
