Modal Parameter Identification Using ARMAV Model

Modal parameter identification using the ARMAV model represents an efficient multi-measurement-point analysis method in structural dynamics. This AutoRegressive Moving Average Vector model can simultaneously process vibration signal data from multiple measurement points, making it particularly suitable for modal analysis of complex structures. In code implementation, this typically involves multi-channel data acquisition and vector-based time series processing algorithms.

The core concept of the ARMAV model involves capturing structural dynamic characteristics through time series modeling. Compared to single-point ARMA models, the ARMAV model accounts for spatial correlations between multiple measurement points, providing a more comprehensive reflection of the structure's overall vibration characteristics. The model parameters directly correspond to key modal parameters such as modal frequencies, damping ratios, and mode shapes. Implementation-wise, this requires setting up vector difference equations that incorporate cross-correlation terms between different measurement channels.

In practical applications, the ARMAV model establishes vector-form difference equations using multi-channel vibration response data, then employs parameter estimation algorithms like least squares to solve for model coefficients. This method can handle response data under stationary excitation and demonstrates good adaptability to non-white-noise excitation scenarios. Model order determination is a critical step in parameter identification, typically requiring optimization selection combined with information criteria such as AIC or BIC. Code implementation often involves iterative algorithms for order selection and parameter optimization.

The advantage of the ARMAV model lies in its ability to extract modal parameters from operational vibration responses without interrupting normal structural usage, making it particularly suitable for long-term health monitoring of large-scale engineering structures like bridges and high-rise buildings. The implementation typically includes automated data processing routines and real-time parameter estimation modules for continuous monitoring applications.