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

Abstract

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.

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

“…However, there are limited reviews on OMP methods, especially in terms of techniques and the performance of the FPGA with emphasis discussion on the implementation of optimisation and LSP. Most reviews broadly discussed the CS reconstruction approach [22], [23] and did not focus on OMP and FPGA implementations. To fill this research gap, the present review focused on the methods of optimisation and least-square problem used in FPGA-based OMP reconstruction algorithm implementations.…”

Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves

“…Mardanshahi et al 17 proposed a machine learning classification model for the automatic detection of matrix cracks in composite materials, and implemented the linear discriminant analysis method to improve the classification accuracy. Wang et al 18 proposed a guided wave detection method based on compressed sensing to detect corrosion and cracks in the structure, which can reduce the amount of guided wave detection data while maintaining the accuracy of defect identification. Peng et al 19 proposed a pipeline protection framework based on distributed fiber sensing technology and machine learning technology to detect the wave propagation and scattering in the pipeline structure including the straight pipe and the elbow pipe.…”

Multihelical Lamb Wave Imaging for Pipe-Like Structures Based on a Probabilistic Reconstruction Approach

“…HE visualization of damage is always the objective of structural health monitoring (SHM) [1]- [3]. The ultrasonic guided wave provides a potential scheme for the inspection and imaging of pipe-like structures for its long propagation distance and low attenuation [4]- [6]. Guided wave can interact with defects in its propagation path and through decoding the information contained in the received signal, the location and size of damage can be obtained [7], [8].…”