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12 pages, 3361 KiB

Open AccessCommunication

A Time-Differential BOCDA Sensor Measurement System Applied to a 1 km Long SMF Using a Semiconductor Optical Amplifier as a Pump Chopper

by Bo-Hun Choi

Sensors 2024, 24(8), 2417; https://doi.org/10.3390/s24082417 - 10 Apr 2024

Viewed by 242

Abstract

A time-differential (TD) Brillouin optical correlation domain analysis (BOCDA) sensor system was applied to measure the Brillouin gain spectrum of a 1 km long sensing optical fiber. The optical delay line used in all BOCDA measurement systems was eliminated in the TD-BOCDA system [...] Read more.

A time-differential (TD) Brillouin optical correlation domain analysis (BOCDA) sensor system was applied to measure the Brillouin gain spectrum of a 1 km long sensing optical fiber. The optical delay line used in all BOCDA measurement systems was eliminated in the TD-BOCDA system by using a bit-delayed modulation relationship between the probe and pump lightwaves. These lightwaves were phase modulated using 2¹⁶-1 pseudo-random binary sequence codes at 5 Gbps. A 2 cm dispersion-shifted fiber placed at the end of the 1 km optical fiber was distinctly identified by the Brillouin frequency extracted from the Brillouin gain spectrum measurement. To investigate the measurement stability of the TD-BOCDA system, experiments were conducted under two different pumping conditions. A semiconductor optical amplifier (SOA) and an intensity modulator (MOD) were compared for the pump chopper used in the TD-BOCDA system to detect the extinction ratio of the pump and the resulting noise in the Brillouin gain measurement. The stability of the Brillouin frequency measurement from the Brillouin gain spectrum in the TD-BOCDA system was investigated by increasing the average value of the measurement using either the SOA or MOD. The repeated-measurement deviation of the system with the SOA was only half of the deviation observed in the system with the MOD. The performance of TD-BOCDA is equivalent to or better than that of conventional BOCDAs in terms of measurement reliability. Moreover, TD-BOCDA is free from the drawbacks of traditional BOCDA, which uses time-delayed fibers and varies the bit rates. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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19 pages, 5290 KiB

Open AccessArticle

A Strain Transfer Model for Detection of Pitting Corrosion and Loading Force of Steel Rebar with Distributed Fiber Optic Sensor

by Jialiang Hu, Fujian Tang, Tianjiao Li, Gang Li and Hong-Nan Li

Sensors 2023, 23(19), 8142; https://doi.org/10.3390/s23198142 - 28 Sep 2023

Viewed by 673

Abstract

Steel rebar corrosion is one of the predominant factors influencing the durability of marine and offshore reinforced concrete structures, resulting in economic loss and the potential threat to human safety. Distributed fiber optic sensors (DFOSs) have gradually become an effective method for structural [...] Read more.

Steel rebar corrosion is one of the predominant factors influencing the durability of marine and offshore reinforced concrete structures, resulting in economic loss and the potential threat to human safety. Distributed fiber optic sensors (DFOSs) have gradually become an effective method for structural health monitoring over the past two decades. In this work, a strain transfer model is developed between a steel rebar and a DFOS, considering pitting-corrosion-induced strain variation in the steel rebar. The Gaussian function is first adopted to describe the strain distribution near the corrosion pit of the steel rebar and then is substituted into the governing equation of the strain transfer model, and the strain distribution in the DFOS is analytically obtained. Tensile tests are also conducted on steel rebars with artificially simulated corrosion pits, which are used to validate the developed model. The results show that the Gaussian function can be used to describe the strain variation near a corrosion pit with a depth less than 50% of the steel rebar diameter, and the strain distribution in the DFOS analytically determined based on the developed strain transfer model agrees well with the tensile test results. The corrosion pit depth and loading force in the steel rebars estimated based on the proposed model agree well with the actual values, and therefore, the developed strain transfer model is effective in detecting pitting corrosion and loading force in steel rebars. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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24 pages, 4226 KiB

Open AccessArticle

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm

by Shuomang Shi, Baiyu Jiang, Simone Ludwig, Luyang Xu, Hao Wang, Ying Huang and Fei Yan

Sensors 2023, 23(17), 7379; https://doi.org/10.3390/s23177379 - 24 Aug 2023

Viewed by 942

Abstract

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a [...] Read more.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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25 pages, 12638 KiB

Open AccessArticle

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

by Li Shang, Zi Zhang, Fujian Tang, Qi Cao, Hong Pan and Zhibin Lin

Sensors 2023, 23(16), 7059; https://doi.org/10.3390/s23167059 - 09 Aug 2023

Cited by 6 | Viewed by 2324

Abstract

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions [...] Read more.

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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22 pages, 11413 KiB

Open AccessArticle

A Novel Smart CFRP Cable Based on Optical Electrical Co-Sensing for Full-Process Prestress Monitoring of Structures

by Huanyu Yang, Lian Shao, Jinping Ou and Zhi Zhou

Sensors 2023, 23(11), 5261; https://doi.org/10.3390/s23115261 - 01 Jun 2023

Viewed by 1144

Abstract

Carbon-fiber-reinforced polymer (CFRP) is a type of composite material with many superior performances, such as high tensile strength, light weight, corrosion resistance, good fatigue, and creep performance. As a result, CFRP cables have great potential to replace steel cables in prestressed concrete structures. [...] Read more.

Carbon-fiber-reinforced polymer (CFRP) is a type of composite material with many superior performances, such as high tensile strength, light weight, corrosion resistance, good fatigue, and creep performance. As a result, CFRP cables have great potential to replace steel cables in prestressed concrete structures. However, the technology to monitor the stress state in real-time throughout the entire life cycle is very important in the application of CFRP cables. Therefore, an optical–electrical co-sensing CFRP cable (OECSCFRP cable) was designed and manufactured in this paper. Firstly, a brief description is outlined for the production technology of the CFRP-DOFS bar, CFRP-CCFPI bar, and CFRP cable anchorage technology. Subsequently, the sensing and mechanical properties of the OECS-CFRP cable were characterized by serious experiments. Finally, the OECS-CFRP cable was used for the prestress monitoring of an unbonded prestressed RC beam to verify the feasibility of the actual structure. The results show that the main static performance indexes of DOFS and CCFPI meet the requirements of civil engineering. In the loading test of the prestressed beam, the OECS-CFRP cable can effectively monitor the cable force and the midspan defection of the beam so as to obtain the stiffness degradation of the prestressed beam under different loads. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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16 pages, 6017 KiB

Open AccessArticle

Guided Wave Ultrasonic Testing for Crack Detection in Polyethylene Pipes: Laboratory Experiments and Numerical Modeling

by Jay Shah, Said El-Hawwat and Hao Wang

Sensors 2023, 23(11), 5131; https://doi.org/10.3390/s23115131 - 27 May 2023

Cited by 6 | Viewed by 2692

Abstract

The use of guided wave-based Ultrasonic Testing (UT) for monitoring Polyethylene (PE) pipes is mostly restricted to detecting defects in welded zones, despite its diversified success in monitoring metallic pipes. PE’s viscoelastic behavior and semi-crystalline structure make it prone to crack formation under [...] Read more.

The use of guided wave-based Ultrasonic Testing (UT) for monitoring Polyethylene (PE) pipes is mostly restricted to detecting defects in welded zones, despite its diversified success in monitoring metallic pipes. PE’s viscoelastic behavior and semi-crystalline structure make it prone to crack formation under extreme loads and environmental factors, which is a leading cause of pipeline failure. This state-of-the-art study aims to demonstrate the potential of UT for detecting cracks in non-welded regions of natural gas PE pipes. Laboratory experiments were conducted using a UT system consisting of low-cost piezoceramic transducers assembled in a pitch-catch configuration. The amplitude of the transmitted wave was analyzed to study wave interaction with cracks of different geometries. The frequency of the inspecting signal was optimized through wave dispersion and attenuation analysis, guiding the selection of third- and fourth- order longitudinal modes for the study. The findings revealed that cracks with lengths equal to or greater than the wavelength of the interacting mode were more easily detectable, while smaller crack lengths required greater crack depths for detection. However, there were potential limitations in the proposed technique related to crack orientation. These insights were validated using a finite element-based numerical model, confirming the potential of UT for detecting cracks in PE pipes. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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21 pages, 10202 KiB

Open AccessArticle

Displacement Monitoring of a Bridge Based on BDS Measurement by CEEMDAN–Adaptive Threshold Wavelet Method

by Chunlan Mo, Huanyu Yang, Guannan Xiang, Guanjun Wang, Wei Wang, Xinghang Liu and Zhi Zhou

Sensors 2023, 23(9), 4268; https://doi.org/10.3390/s23094268 - 25 Apr 2023

Cited by 1 | Viewed by 1142

Abstract

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method [...] Read more.

From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data processing method of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), combined with adaptive threshold wavelet de-noising is proposed. The adaptive threshold wavelet filtering method composed of the mean and variance of wavelet coefficients of each layer is used to de-noise the BDS displacement monitoring data. CEEMDAN was used to decompose the displacement response data of the bridge to obtain the intrinsic mode function (IMF). Correlation coefficients were used to distinguish the noisy component from the effective component, and the adaptive threshold wavelet de-noising occurred on the noisy component. Finally, all IMF were restructured. The simulation experiment and the BDS displacement monitoring data of Nanmao Bridge were verified. The results demonstrated that the proposed method could effectively suppress random noise and multipath noise, and effectively obtain the real response of bridge displacement. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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Review

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25 pages, 5508 KiB

Open AccessReview

Fiber Optic-Based Durability Monitoring in Smart Concrete: A State-of-Art Review

by Hou Qiao, Zhen Lin, Xiangtao Sun, Wei Li, Yangping Zhao and Chuanrui Guo

Sensors 2023, 23(18), 7810; https://doi.org/10.3390/s23187810 - 11 Sep 2023

Cited by 3 | Viewed by 1225

Abstract

Concrete is the most commonly used construction material nowadays. With emerging cutting-edge technologies such as nanomaterials (graphene, carbon nanotubes, etc.), advanced sensing (fiber optics, computer tomography, etc.), and artificial intelligence, concrete can now achieve self-sensing, self-healing, and ultrahigh performance. The concept and functions [...] Read more.

Concrete is the most commonly used construction material nowadays. With emerging cutting-edge technologies such as nanomaterials (graphene, carbon nanotubes, etc.), advanced sensing (fiber optics, computer tomography, etc.), and artificial intelligence, concrete can now achieve self-sensing, self-healing, and ultrahigh performance. The concept and functions of smart concrete have thus been partially realized. However, due to the wider application location (coastal areas, cold regions, offshore, and deep ocean scenarios) and changing climate (temperature increase, more CO₂ emissions, higher moisture, etc.), durability monitoring (pH, ion penetration, carbonation, corrosion, etc.) becomes an essential component for smart concrete. Fiber optic sensors (FOS) have been widely explored in recent years for concrete durability monitoring due to their advantages of high sensitivity, immunity to harsh environments, small size, and superior sensitivity. The purpose of this review is to summarize FOS development and its application in concrete durability monitoring in recent years. The objectives of this study are to (1) introduce the working principle of FOS, including fiber Bragg grating (FBG), long-period fiber grating (LPFG), surface plasmon resonance (SPR), fluorescence-based sensors, and distributed fiber optic sensors (DFOS); (2) compare the sensitivity, resolution, and application scenarios of each sensor; and (3) discuss the advantages and disadvantages of FOS in concrete durability monitoring. This review is expected to promote technical development and provide potential research paths in the future for FOS in durability monitoring in smart concrete. Full article

(This article belongs to the Special Issue Optical Fiber Sensor Technology for Structural Health Monitoring)

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