Which algorithms are used in Operational Modal Analysis?

Stochastic Subspace Identification (SSI) is one of the most widely used methods in OMA. It is a time-domain technique that estimates modal parameters based on state-space models derived from measured response data.

SSI is commonly used for:

• structures with closely-space modes caused by the structure’s symmetry
• high-quality modal identification
• automated analysis using stabilization diagrams

Frequency Domain Decomposition (FDD) is a frequency-domain method that identifies modal parameters by singular value decomposing the spectral density matrix of measured responses.

FDD is typically used for:

• initial identification of natural frequencies and mode shapes
• simple and intuitive analysis
• visual interpretation of mode shapes

Enhanced Frequency Domain Decomposition (EFDD) extends FDD by enabling estimation of damping ratios and more accurate modal parameters.

EFDD is often used when:

• damping estimation is required
• improved natural frequency and mode shape estimation
• more detailed and validated modal characterization is needed

The choice of algorithm depends on the structure, data quality, and analysis objectives.

• SSI is often preferred for robust and automated identification
• FDD is useful for quick overview and initial analysis
• EFDD provides improved accuracy for detailed studies

In practice, multiple methods are often used together to validate results.

OMA algorithms are implemented in specialized modal analysis software.

Methods such as SSI, FDD, and EFDD are available in tools like ARTeMIS Modal Pro, which provides workflows for modal identification, validation, and visualization.