Damage detection in flexible plates through reduced-order modeling and hybrid particle-Kalman filtering

Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated

A multiscale approach to the smart deployment of micro-sensors over flexible plates

In former studies, we proposed a topology optimization approach to maximize the sensitivity to damage of measurements collected through a network of sensors deployed over flexible, thin plates. Within such frame, damage must be intended as a change of the structural health characterized by a reduction of the relevant load-carrying capacity. By properly comparing the response of the healthy, undamaged structure and the response of the damaged one, independently of the location of the source of damage, a procedure to optimally deploy a given set of sensors was provided. In this work we extend the aforementioned approach within a multiscale frame, to account for (at least) three different length-scales: a macroscopic one, linked to the dimensions of the structure to be monitored; a mesoscopic one, linked to the characteristic size of the damaged region(s); a microscopic one, linked to the size of inertial microelectromechanical systems (MEMS) to be used within a marginally invasive health monitoring system. Results are provided for a square plate fully clamped along its border, to show how the micro-sensors are to be deployed to maximize the sensitivity of measurements to damage, and to also discuss the speedup obtained with the proposed multiscale approach in comparison with a standard single-scale one

Structural health monitoring by combining machine learning and dimensionality reduction techniques

Article number 20International audienceStructural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed

Features of cross-correlation analysis in a data-driven approach for structural damage assessment

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.Peer ReviewedPostprint (published version