Guided waves have gained popularity in structural health monitoring (SHM) due to their ability to inspect large areas with little attenuation, while providing rich interactions with defects. For thin-walled structures, the propagating waves are Lamb waves, which are a complex but well understood type of guided waves. Recent works have cast the defect localization problem of Lamb wave based SHM within the sparse reconstruction framework. These methods make use of a linear model relating the measurements with the scene reflectivity under the assumption of point-like defects. However, most structural defects are not perfect points but tend to assume specific forms, such as surface cracks or internal cracks. Knowledge of the "type" of defects is useful in the assessment phase of SHM. In this paper, we present a dual purpose sparsity-based imaging scheme which, in addition to accurately localizing defects, properly classifies the defects present simultaneously. The proposed approach takes advantage of the bias exhibited by certain types of defects toward a specific Lamb wave mode. For example, some defects strongly interact with the anti-symmetric modes, while others strongly interact with the symmetric modes. We build model based dictionaries for the fundamental symmetric and anti-symmetric wave modes, which are then utilized in unison to properly localize and classify the defects present. Simulated data of surface and internal defects in a thin Aluminum plate are used to validate the proposed scheme.
A multimodal sparse reconstruction approach is proposed for localizing defects in thin plates in Lamb wave-based structural health monitoring. The proposed approach exploits both the sparsity of the defects and the multimodal nature of Lamb wave propagation in plates. It takes into account the variation of the defects’ aspect angles across the various transducer pairs. At low operating frequencies, only the fundamental symmetric and antisymmetric Lamb modes emanate from a transmitting transducer. Asymmetric defects scatter these modes and spawn additional converted fundamental modes. Propagation models are developed for each of these scattered and spawned modes arriving at the various receiving transducers. This enables the construction of modal dictionary matrices spanning a two-dimensional array of pixels representing potential defect locations in the region of interest. Reconstruction of the region of interest is achieved by inverting the resulting linear model using the group sparsity constraint, where the groups extend across the various transducer pairs and the different modes. The effectiveness of the proposed approach is established with finite-element scattering simulations of the fundamental Lamb wave modes by crack-like defects in a plate. The approach is subsequently validated with experimental results obtained from an aluminum plate with asymmetric defects.
Pipes are used for the transport of fluids and gases in urban and industrial settings, such as buried pipelines to transport water, oil, and other resources. To ensure reliable operation, it is essential that an inspection system be in place to identify and localize damage/defects in the pipes. Unfortunately, many of the typical nondestructive evaluation techniques are inadequate due to limited pipe access; often, only the beginning and end sections of the pipe are physically accessible. As such, this problem is well suited to the use of ultrasonic guided-wave based structural health monitoring. With a limited number of transducers, ultrasonic guided waves can be used to interrogate long lengths of pipes. In this paper, we propose a damage detection and localization scheme that relies upon the inherent sparsity of defects in the pipes. A sparse array of transducers, deployed in accessible areas of the pipes, is utilized in pitch-catch mode to record signals scattered by defects in the pipe. Both the direct path scattering off the defect, and the helical modes, which are paths that spiral around the circumference of the pipe before or after interaction with the defect, are recorded. A Lamb wave based signal model is formulated that accounts for this multipath approach. The signal model is then inverted via group sparse reconstruction, in order to produce an image of the scene. The model accounts for the specificities of Lamb wave propagation through the pipe. Performance validation of the proposed approach is provided using simulated data for an aluminum pipe.
The presence of multiple modes in guided-wave structural health monitoring has been usually considered a nuisance and a variety of methods have been devised to ensure the presence of a single mode. However, valuable information regarding the nature of defects can be gleaned by including multiple modes in image recovery. In this paper, we propose an effective approach for localizing defects in thin plates, which involves inversion of a multimodal Lamb wave based model by means of sparse reconstruction. We consider not only the direct symmetric and anti-symmetric fundamental propagating Lamb modes, but also the defect-spawned mixed modes arising due to asymmetry of defects. Model-based dictionaries for the direct and spawned modes are created, which take into account the associated dispersion and attenuation through the medium. Reconstruction of the region of interest is performed jointly across the multiple modes by employing a group sparse reconstruction approach. Performance validation of the proposed defect localization scheme is provided using simulated data for an aluminum plate.
In structural health monitoring, Lamb waves are employed extensively to examine and monitor thin structures, such as plates and shells. Typically, a network of piezoelectric transducers is attached to the structural plate member and used for both transmission and reception of the Lamb waves. The signals scattered from defects in the plate are recorded by employing the transducers in pitch-catch pairings. In this paper, we propose a multi-frequency, multi-modal sparse reconstruction approach for localizing defects in thin plates. We simultaneously invert Lamb wave based scattering models for both fundamental propagating symmetric and anti-symmetric wave modes, while exploiting the inherent sparsity of the defects. Dictionaries are constructed for both fundamental wave modes, which account for associated dispersion and attenuation as a function of frequency. Signals are collected at two independent frequencies; one at which the fundamental symmetric mode is dominant, and the other at which only the fundamental anti-symmetric wave mode is present. This provides distinct and separable multi-modal contributions, thereby permitting sparse reconstruction of the region of interest under the multiple measurement vector framework. The proposed defect localization approach is validated using simulated data for an aluminum plate.
Lamb waves are utilized extensively for structural health monitoring of thin structures, such as plates and shells. Normal
practice involves fixing a network of piezoelectric transducers to the structural plate member for generating and
receiving Lamb waves. Using the transducers in pitch-catch pairs, the scattered signals from defects in the plate can be
recorded. In this paper, we propose an l<sub>1</sub>-norm minimization approach for localizing defects in thin plates, which inverts
a multimodal Lamb wave based model through exploitation of the sparseness of the defects. We consider both symmetric
and anti-symmetric fundamental propagating Lamb modes. We construct model-based dictionaries for each mode, taking
into account the associated dispersion and attenuation through the medium. Reconstruction of the area being
interrogated is then performed jointly across the two modes using the group sparsity constraint. Performance validation
of the proposed defect localization scheme is provided using simulated data for an aluminum plate.