Abstract
The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.
Highlights
In the field of condition monitoring, model-based fault diagnosis (FD) [1,2,3] has been gaining ground, as a robust means for condition assessment
We address these issues by proposing a distributed framework for structural identification, damage detection and localization
Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization
Summary
In the field of condition monitoring, model-based fault diagnosis (FD) [1,2,3] has been gaining ground, as a robust means for condition assessment. Detailed representations using finite/discrete element models or multibody representations tend to suffer from the curse of uncertainty, tied to the required definition of modeling parameters, and will typically come with considerable computational toll This is true in the modeling of wind turbines components, where, due to the complexity of the involved geometry and materials, the establishment of a structural model forms an intricate task [12]. A two-stage algorithmic process is proposed in this work, with the aim (i) to construct a data-driven model of the structure, able to reliably reconstruct the response across the full range of operation of the system in its healthy state and (ii) reliably detect and possibly localize damage, via use of a suitably defined Residual Generation Algorithm (RGA) [1,61,62,63,64] featuring appropriately selected detection thresholds.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
