Andrew Golato

Multimodal sparse reconstruction in Lamb wave-based structural health monitoring

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 l1-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.

Multimodal sparse reconstruction in guided wave imaging of defects in plates

Abstract. 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.

Multimodal exploitation and sparse reconstruction for guided-wave structural health monitoring

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.

Sparsity based defect imaging in pipes using guided waves

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.

Multifrequency and multimodal sparse reconstruction in Lamb wave based structural health monitoring

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.

Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning

In this paper, we propose the exploitation of sparse Bayesian learning in multiple-input multiple-output (MIMO) systems to account for the multi-dimensional group sparse nature of extended defects in guided ultrasonic wave based structural health monitoring. The multi-dimensional group sparsity in the underlying reconstruction problems arises due to the clustered spatial occupancy of extended defects and the multiple-aspect MIMO observations. Sparse Bayesian learning techniques have been shown to provide robustness for high-resolution signal reconstruction due to their insensitivity to dictionary coherence and have the flexibility of effective exploitation of the signal structure. The superiority of the proposed technique over the state-of-the-art sparse signal reconstruction techniques is demonstrated through simulations and preliminary experiments.

Multi-path Exploitation in a Sparse Reconstruction Approachto Lamb Wave Based Structural Health Monitoring

In guided-wave structural health monitoring, the multipath reflections due to target interactions with the plate boundaries have typically been avoided. This has been accomplished either by examining only the interior regions of the plate under test, whereby boundary reflections arrive outside the considered time window, or through application of dampening clay, to the boundaries that renders the amplitude of the multipath reflections negligible. While both methods are successful, they fail to represent a robust realistic scenario, and ignore the potentially beneficial contributions of multipath. This paper proposes a multipath exploitation scheme for defect localization in guided Lamb wave structural health monitoring under the sparse reconstruction framework. More specifically, a single-mode Lamb wave based signal model, which accounts for both multipath reflections and the direct target scatterings, is inverted via group sparse reconstruction. Performance validation of the proposed defect localization scheme is provided using simulated data for an aluminum plate. doi: 10.12783/SHM2015/240