Pipeline monitoring based on ultrasonic guided acoustic wave and fiber optic sensor fusion

Lalam, Nageswara; Wright, Ruishu; Ohodnicki, Paul R.

doi:10.1117/12.2619042

Cited by 8 publications

(12 citation statements)

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“…Specifically, in this equation provided, n is the number of cycles of the signal that should be included in the excitation (based upon the Hanning window (period)). The assumed excitation amplitude is 0.003 inches, based on calibration between piezo actuator voltage and simulation excitation displacement from past work [8], [9]. This signal was defined in ANSYS to simulate the effects of an actuator, which is a representative excitation waveform when compared with existing commercial ultrasonic acoustic NDE technologies [24], [26] The corresponding wave in the frequency domain.…”

Section: The Excitation Concept Of Guided Ultrasonic Waves (Ugws)mentioning

confidence: 99%

“…Current fully distributed DAS interrogation systems are not capable of operating at frequencies of 30-50kHz. So, in the current study we use a representative example of the magnitude of experimental noise derived from a previously demonstrated experimental set-up having sufficient frequency bandwidth for ultrasonic acoustic guided wave monitoring, and its resulting signal with a 32kHz excitation as illustrated in Figures 7 (a) and (b) [8]. In this case, a quasi-distributed multimode interferometric structure is assumed.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Here, Psignal is the experimental signal after passband filtering the raw data set as per our prior publications [8], [35]. The noise signal, Pnoise, can be extracted from the received signal by applying wavelet transformation to isolate different frequency components, followed by bandpass filtering to further remove unwanted frequency components outside the specified range.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…In the past decades, distributed fiber vibration/acoustic sensor technology has gained increasing attention and tremendous growth. The quasi-distributed/point fiber vibration/acoustic sensor technologies include fiber Bragg grating (FBG) [6], Fabry-Pérot [7], and Multimodal interference [8]. Various interferometric configurations include; the Sagnac interferometer [9], the Mach-Zehnder interferometer (MZI) [10], and polarization-OTDR (POTDR) [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: The Excitation Concept Of Guided Ultrasonic Waves (Ugws)mentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

Section: Data Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

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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show abstract

“…Therefore, our simulations assume a quasi-distributed acoustic sensing (qDAS) scheme utilizing Fiber Bragg Gratings, in-line Fabry-Perot interferometers, or other interferometric structures [15]. qDAS systems can achieve smaller effective gauge length, improved optimization for measuring UGW features, and higher bandwidth acoustic frequency sensing when compared with DAS systems, as required for UGW monitoring [16] [17].…”

Section: Sensor Measurement Modelmentioning

confidence: 99%

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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Experimental Test of Pressure Variation and Leakage Location in Petroleum Pipeline Leakage Process

Hu¹,

Wang

Zhi

et al. 2023

J Fail. Anal. and Preven.

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Pipeline monitoring based on ultrasonic guided acoustic wave and fiber optic sensor fusion

Cited by 8 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

Experimental Test of Pressure Variation and Leakage Location in Petroleum Pipeline Leakage Process

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