A quality-based automated procedure for operational modal analysis

Many efforts have been made in the last decade to define Automated Operational Modal Analysis (AOMA) procedures able to process large datasets from long-term monitoring systems. However, some issues are still open and further studies on this topic are needed; in particular following points should be better investigated: i) controlling the tuning parameters to minimize the modelling errors, ii) defining suitable automatization method for processing large datasets and iii) validating the extracted modal parameters. These points are investigated in this paper with the aim of enhancing the current AOMA procedures based on the Stochastic Subspace Identification (SSI) techniques, and the following novelties are introduced: i) a minimization approach for the tuning of the initial parameters in the SSI algorithm, ii) a statistical method to automatically define the cut-off threshold in the hierarchical clustering phase, and iii) a Modal Quality Index (MQI) – ranging from 0 to 1 – to validate the identified modes. The above novelties represent key aspects that allow a real-time check of the modal parameters provided by a monitoring system within a Continuous Structural Health monitoring (CSHM) framework.