Feature extraction for pipeline defects inspection based upon distributed acoustic fiber optic sensing data

Zhang, Pengdi; Venketeswaran, Abhishek; Wright, Ruishu; Denslow, Kayte M.; Babaee, Hessam; Ohodnicki, Paul R.

doi:10.1117/12.2618263

Cited by 7 publications

(14 citation statements)

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“…For the numerical simulation of guided wave-based pipe structures, the selection of analysis steps and time increments relies on the system properties (including dimensions and excitation signal), as well as the sampling interval and frequency of detection signals. The Transient Structural Module, which is a dynamic implicit analysis step in ANSYS, was chosen to examine the problem of integrating damage identification for pipe structures with fiber optic sensor deployment [4]. The guided wave propagation can be derived from the Navier-Lame equation [19]:…”

Section: Analysis Setup and Parameter Designmentioning

confidence: 99%

“…So, the maximum time increment should be smaller than 1/20 of the excitation signal period corresponding to the highest frequency. In this paper, the highest frequency of the excitation signal is 50 kHz, so it can be obtained that the time increment should be equal or less than 1 µs by equation (4). Considering the accuracy of the analytical results, 1 µs is chosen as the increment.…”

Section: Analysis Setup and Parameter Designmentioning

confidence: 99%

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Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

Zhang¹,

Venketeswaran²,

Wright³

et al. 2023

Preprint

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This study proposes a machine-learning-based framework for detecting mechanical damage in pipelines, utilizing physics-informed datasets collected from simulations for mechanical damage. The framework provides an effective workflow from dataset generation to damage detection and identification for three types of pipeline events: welds, clamps, and corrosion defects. While the study initially focused on optimizing the CNN structure using various advanced optimizers, it also investigated the impact of sensing systems on data classification and the effect of noise on classification performance. The study's analysis highlights the importance of selecting the appropriate sensing system for the specific application. The authors also found that the proposed framework is robust to experimentally relevant levels of noise, suggesting its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage in pipelines. The proposed framework provides an effective workflow for damage detection and identification, and the findings on the impact of sensing systems and noise on classification performance add to its robustness and reliability.

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Section: Analysis Setup and Parameter Designmentioning

confidence: 99%

Section: Analysis Setup and Parameter Designmentioning

confidence: 99%

Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

Zhang¹,

Venketeswaran²,

Wright³

et al. 2023

Preprint

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“…The Lamé parameters (λ and μ) are related to the material's elastic properties, specifically the Young's modulus (E) and Poisson's ratio (ν) [7]. Alternatively, we can write the wave equation in matrix form, which allows us to solve the problem using linear algebra techniques.…”

Section: Finite Element Analysis Of Lamb Wave Propagationmentioning

confidence: 99%

“…By examining the dispersion curve for a specific pipeline scenario, the central frequency and wave mode of the excitation signal can be determined. This approach enables the preferential excitation of modes that may enhance resolution and defect sensitivity, as discussed in previous research [7].Figure 3 During the construction of the finite element model for the steel pipe structure, it is essential to first establish the dimensions and material properties of the pipe and the excitation source. The material and dimensional parameters for the investigated pipeline structure can be found in Tables 1 and 2, respectively.…”

Section: Dimension Parameters Of Object Pipelinementioning

confidence: 99%

“…This approach can be integrated into an AI-driven data analytics framework for defect classification, ultimately allowing for more accurate simulations and characterizations of guided wave propagation in monitored pipelines than traditional ultrasonic acoustic NDE methods. Figure 1 highlights the overall schematic for our proposed approach, encompassing simulation technology, feature selection [7], and prediction with reduced-order dataset construction. This work concentrates on simulating guided wave propagation along pipelines, extracting guided wave signals from the pipeline surface, and quantifying various signal features for differentiating between defect classes.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning data analytics based on distributed fiber sensors for pipeline feature detection

Zhang

Venketeswaran

Bukka

et al. 2023

Optical Waveguide and Laser Sensors II

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Distributed acoustic fiber optic sensors (DAS) enable spatially distributed monitoring of perturbations and contain rich multidimensional information that can be used in structural health monitoring. Machine learning based on physics-based simulations can make a breakthrough in traditional data analysis methods to improve their efficiency and performance, solving a series of problems such as huge data volume, low data processing speed, data signal-to-noise ratio, etc. Here, the relationship of DAS response and corrosion type are studied.First, we present a systematic theoretical study of the potential of direct coupling of quasi-distributed acoustic sensing (q-DAS) with guided ultrasound typically used for real-time pipeline health monitoring. To investigate properties of scattered acoustic waves and the performance of DAS and q-DAS in identifying defects, we use finite element analysis to simulate the response in a variety of pipeline structures including welds, clamps, defect types, and sensor installations representing various corrosion patterns expected in practice. A specific emphasis will be placed upon simulating and modeling pitting corrosion defects and contrasting with other types of corrosion observed in practice. We also aim to compare and analyze signal characteristics due to different kinds of corrosion types and structures, and to enhance machine learning algorithms for detection and size prediction of major pipeline structural changes and corrosion types. Ultimately, results of simulated DAS and q-DAS sensor networks are analyzed by a neural network-based machine learning algorithm for defect identification through supervised learning. To evaluate and improve effectiveness, we estimate model uncertainty and identify features of simulated results that contribute most to the model performance and efficacy.

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Pipeline Leaks Early Warning Based on Distributed Optical Fiber Sensing Technology

Jia,

Sun,

Duan

et al. 2024

IEEE Photon. Technol. Lett.

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Feature extraction for pipeline defects inspection based upon distributed acoustic fiber optic sensing data

Cited by 7 publications

References 0 publications

Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

Real-Time Pipe Structure Change Detection and Classification using Distributed Acoustic Fiber Sensors Based on Convolutional Neural Network (CNN) Models

Machine learning data analytics based on distributed fiber sensors for pipeline feature detection

Pipeline Leaks Early Warning Based on Distributed Optical Fiber Sensing Technology

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