Full Wavefield Analysis and Damage Imaging Through Compressive Sensing in Lamb Wave Inspections

Huang

IEEE Trans. Instrum. Meas.

et al. 2020

The pipeline in-service needs to be inspected in certain period to master its structural health status. Ultrasonic guided wave which can propagate along pipelines with less energy loss provides an efficient method for long-term in situ inspection. Guided wave can detect both corrosion and cracks existing in structures. To overcome the problem of huge amounts of data and to maintain defect identification accuracy, the compressed sensing method for guided wave inspection is proposed. The compression process is essentially a scheme of analog to information conversion to compress the signal. It is accomplished by random demodulation and the equivalent sampling rate below the Nyquist rate helps to save most of storage. The compressed data is recovered to the sparse spatial domain based on the constructed dictionary from a guided wave propagation model. To verify the effectiveness of proposed method, both numerical simulations and experimental investigations are conducted. The results indicate the availability of compression and high accuracy of defect location after recovery. The influences of different compression schemes and compression ratios are further analyzed. In addition, the comparisons with direct recovery without compression and traditional analysis methods demonstrate the advantageous performance of the proposed method.

Section: Related Workmentioning

confidence: 99%

Compressed Sensing Method for Health Monitoring of Pipelines Based on Guided Wave Inspection

Huang

IEEE Trans. Instrum. Meas.

et al. 2020

“…As an excellent signal reconstruction strategy, compressed sensing (CS) has attached much attention in the guided wave based NDT and SHM [17]. Based on the sparse prior of Lamb waves in a specified base, some sparse reconstruction algorithms have been developed to realize the reconstruction of the dispersion curve [18], wavefield [19], amplitude [20], and to locate damage [21]. Harley and Moura [18,22] extracted the wave number sparsity in the frequency wave number domain to reconstruct the dispersion relation.…”

Section: Introductionmentioning

confidence: 99%

Sparse Reconstruction and Damage Imaging Method Based on Uniform Sparse Sampling

Luo

Feng

et al. 2020

Acta Mech. Solida Sin.

The full wavefield detection method based on guided waves can efficiently detect and locate damages relying on the collection of large amounts of wavefield data. The acquisition process by scanning laser Doppler vibrometer (SLDV) is generally time-consuming, which is limited by Nyquist sampling theorem. To reduce the acquisition time, full wavefield data can be reconstructed from a small number of random sampling point signals combining with compressed sensing. However, the random sampling point signals need to be obtained by adding additional components to the SLDV system or offline processing. Because the random sparse sampling is difficult to achieve via the SLDV system, a new uniform sparse sampling strategy is proposed in this paper. By using the uniform sparse sampling coordinates instead of the random spatial sampling point coordinates, sparse sampling can be applied to SLDV without adding additional components or offline processing. The simulation and experimental results show that the proposed strategy can reduce the measurement locations required for accurate signal recovery to less than 90% of the Nyquist sampling grid, and the damage location error is within the minimum half wavelength. Compared with the conventional jittered sampling strategy, the proposed sampling strategy can directly reduce the sampling time of the SLDV system by more than 90% without adding additional components and achieve the same accuracy of guided wavefield reconstruction and damage location as the jittered sampling strategy. The research results can greatly improve the efficiency of damage detection technology based on wavefield analysis.

“…The goal of providing early anomaly detection and damage localization is pivotal in SHM [4][5][6][7][8][9]. Compactness and reduced power consumption make microelectromechanical (MEMS) sensors suitable for structural monitoring; also, they can be directly deployed on-structure, all the while allowing for low-cost frameworks and extending electronics life cycle.…”

Section: Introductionmentioning

confidence: 99%

A Tilt Sensor Node Embedding a Data-Fusion Algorithm for Vibration-Based SHM

et al. 2019

Self Cite

This work describes a miniaturized sensor network based on low-power, light-weight and small footprint microelectromechanical (MEMS) sensor nodes capable to simultaneously measure tri-axial accelerations and tri-axial angular velocities. A real-time data fusion algorithm based on complementary filters is applied to extract tilt angles. The resulting device is designed to show competitive performance over the whole frequency range of the inertial units. Besides the capability to provide accurate measurements both in static and dynamic conditions, an optimization process has been designed to efficiently make the fusion procedure running on-sensor. An experimental campaign conducted on a pinned-pinned steel beam equipped with a network comprising several sensor nodes was used to evaluate the reliability of the developed architecture. Performance metrics revealed a satisfactory agreement to the physical model, thus making the network suitable for real-time tilt monitoring scenarios.