Fully Automated Operational Modal Analysis using multi-stage clustering

Abstract

The interest for robust automatic modal parameter extraction techniques has increased significantly over the last years, together with the rising demand for continuous health monitoring of critical infrastructure like bridges, buildings and wind turbine blades. In this study a novel, multi-stage clustering approach for Automated Operational Modal Analysis (AOMA) is introduced. In contrast to existing approaches, the procedure works without any user-provided thresholds, is applicable within large system order ranges, can be used with very small sensor numbers and does not place any limitations on the damping ratio or the complexity of the system under investigation. The approach works with any parametric system identification algorithm that uses the system order n as sole parameter. Here a data-driven Stochastic Subspace Identification (SSI) method is used.Measurements from a wind tunnel investigation with a composite cantilever equipped with Fiber Bragg Grating Sensors (FBGSs) and piezoelectric sensors are used to assess the performance of the algorithm with a highly damped structure and low signal to noise ratio conditions. The proposed method was able to identify all physical system modes in the investigated frequency range from over 1000 individual datasets using FBGSs under challenging signal to noise ratio conditions and under better signal conditions but from only two sensors.

Automated Modal Identification Based on Improved Clustering Method

“…e main work in automated stabilization diagram interpretation is to automatically eliminate the spurious modal data, which originates from the overestimation of system orders, and pick out the truly physical modal results. e automated stabilization diagram interpretation process is accomplished by clustering method [11,14,[16][17][18][19][20][21], especially hierarchical clustering method [11,16,19,20]. For the clustering process, two critical problems, namely, the proper clustering threshold and the precision assessment of the clustering results, need to be carefully dealt with.…”

“…Reynders et al [16] proposed a method to automatically calculate the clustering threshold to make the modal identification process fully automated, and this method was later validated in many cases [19,20]. However, the precision of the automated calculated threshold cannot be guaranteed.…”

“…However, the precision of the automated calculated threshold cannot be guaranteed. As a development, Neu et al [19] later improved the whole clustering process and claimed that the parameters used in discriminating the mathematical modal data should be carefully selected and pointed out that the precision of the automatically calculated clustering threshold in [16] is challenging. Sun et al [20] also proposed an approach to determine the clustering threshold and applied it to a cablestayed bridge to automatically identify the modal parameters, but it lacks some physical explanation.…”

“…Another limitation of all the automated modal identification methods is that the precision of the clustering results cannot be assessed. Even though an outlier detection method was proposed in [19] to detect the outliers of clustering results, it was conducted based on a prior distribution assumption, which does not meet the reality of automated modal identification.…”

Automated peak picking using region‐based convolutional neural network for operational modal analysis

“…The modal parameters calculated from SSI or ERA with different system orders are grouped based on their similarity to each other, from which the frequently obtained are selected as the physical modes . Neu et al and Cabboi et al further improved the approach in terms of automation by minimizing user‐defined parameters. In addition, Afshar and Khodaygan presented an enhanced stabilization chart for automated modal analysis.…”