The modes of linear time invariant mechanical systems can be estimated from output-only vibration measurements under ambient excitation conditions with subspace-based system identification methods. In the presence of additional unmeasured periodic excitation, for example due to rotating machinery, the measurements can be described by a state-space model where the periodic input dynamics appear as a subsystem in addition to the structural system of interest. While subspace identification is still consistent in this case, the periodic input may render the modal parameter estimation difficult, and periodic modes often disturb the estimation of close structural modes. The aim of this work is to develop a subspace identification method for the estimation of the structural parameters while rejecting the influence of the periodic input. In the proposed approach, the periodic information is estimated from the data with a non-steady state Kalman filter, and then removed from the original output signal by an orthogonal projection. Consequently, the parameters of the periodic subsystem are rejected from the estimates, and it is shown that the modes of the structural system are consistently estimated. Furthermore, standard data analysis procedures, like the stabilization diagram, are easier to interpret. The proposed method is validated on Monte Carlo simulations and applied to both a laboratory example and a full-scale structure in operation.

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Summary In the context of detecting changes in structural systems, several vibration‐based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. These methods involve computing data‐based features, which are then evaluated in statistical tests to detect damages. While being sensitive to damages, the data‐based features are affected by changes in the ambient excitation properties that potentially lead to false alarms in the statistical tests, a characteristic that renders their use impractical for structural monitoring. In this paper, a damage detection method is presented that is robust to changes in the covariance of the ambient excitation. The proposed approach is based on the Mahalanobis distance of output covariance Hankel matrices, which are normalized with respect to possibly changing excitation properties. The statistical properties of the developed damage feature are reported and used for efficient hypothesis testing. Its robustness towards changes in the excitation covariance is illustrated on numerical simulations and successfully tested on a numerical offshore foundation model.

The Modal Assurance Criterion (MAC) is a modal indicator designed to decide whether the mode shapes used in its computation are corresponding to the same mode. During structural monitoring, it can be applied to evaluate changes in the mode shapes. When the mode shapes are estimated from measurement data, the MAC inherits their statistical properties, thus is afflicted with statistical uncertainty. The evaluation of this uncertainty is particularly relevant when the MAC estimate is close to 1, where 1 indicates equal mode shapes. In structural monitoring, it can be used to assess changes in mode shapes after early damage.While the framework for uncertainty quantification of modal parameters is well-known and developed in the context of subspace-based system identification methods, uncertainty quantification for the MAC has not been developed yet. A particular challenge for its statistical characterization is its boundedness in the interval between 0 and 1. In this paper it is shown that this boundedness yields two different distributions of the MAC estimates. The MAC computed between estimates of different mode shapes is inside the interval (0, 1), and a Gaussian approximation of its distribution is obtained. When the MAC is computed between estimates of equal mode shapes, the resultant MAC estimate is close to 1, and the classical Gaussian approximation is inadequate. In this case it is shown that the MAC estimate is linked to a quadratic form of the mode shapes, whose distribution can be approximated by a scaled and shifted χ 2 distribution. For both cases, uncertainty bounds related to the MAC estimates are established. The proposed frameworks are validated by extensive Monte Carlo simulations and then applied to evaluate mode shape changes due to damage during monitoring of the S101 Bridge.

A presence of a high amplitude periodic signals in the output responses from operating structures often pose a challenge for output-only system identification and, in case of health monitoring, damage detection/localization methods. This paper introduces a pre-processing approach that removes the harmonic part from the output signals directly in the time domain. The new method uses orthogonal projections of the harmonic realization of the signal onto the raw time series within the stochastic subspace framework. Proposed algorithm is tested on two experimental examples. First, an aluminum plate excited with both random white and periodic excitations. Second, a full-scale industrial case of a ferry excited by a random environmental load with harmonic interference from a rotating machinery on-board. In both cases the proposed method removes the harmonics from the structural responses while leaving the random part of the output signal.