Operational Modal Analysis (OMA)

Operation Modal Analysis with WaveImage

This module can be used to analyze structures under stochastic or ambient excitation (e. g. wind, traffic), where a measurable excitation is complicated or impossible, e.g. structures are too small or too large. Operational Modal Analysis (OMA) is also known as ambient modal analysis, ambient vibration testing or output-only modal analysis. It uses the dynamic response of a structure on-site under its environmental conditions to determine its modal parameters (natural frequencies, mode shapes and damping).

Various time-based algorithms are available for the extraction of the modal parameters. The evaluation of the vibration analysis is possible without expert knowledge, since suitable evaluation parameters are automatically determined from the input data.

Fast and Automated Modal Analysis

Natural frequency and resonance
Damping
Mode shapes

Algorithms

State-of-the-art modal analysis algorithms in time and frequency domain for single and multiple setups

Frequency Domain:

Complex Mode Indicator Function (CMIF)
Operational Polyrefrence Least Squares Complex Frequency (Operational Poly-LSCF)

Time Domain:

Unweighted Principal Component (SSI*- UPC)
Principal Component (SSI*- PC)
Canonical Variate Analysis (SSI*- CVA)
Covariance Analysis (SSI*- COV)

*SSI = Stochastic Subspace Identification

Support of Multi-Setup

Supported strategies:

PoSER (Post Separate Estimation Re-scaling)
PoGER (Post Global Estimation Re-Scaling)
PreGER (Pre Global Estimation Re-Scaling)

Views

Different views for animation:

3D geometry
3D geometry + photo
Photo only

Different views for evaluation:

Complexity plot
Modal Assurance Criterion (2D and 3D)
Order traces like frequency, damping, frequency vs. damping and complexity

Methods

Order-Based:

Optimized and extreme clear stability chart with very high accuracy of physical modes and removed noise
Use of projection channels reduce the calculation overhead and guarantees high accuracy in modal parameter estimation
Projection channels can be found manually or fully automatic
Modal parameter estimation in time and frequency domain
Single or multiple test setups possible
Very fast estimation of big data models
Very limited user interaction is required

Non-Order-Based:

Automatic peak finding using artificial intelligence
No parameters required to find peaks
Very fast and memory save estimation
Single or multiple test setups possible
Use of projection channels reduce the calculation overhead and guarantees high accuracy in modal parameter estimation
Projection channels can be found manually or fully automatic
Global modal parameter estimation can be made manually or automatic