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This article presents a robust methodology for long-term offshore structural health monitoring (SHM) using the Global Navigation Satellite Systems (GNSS). The methodology relies on recently developed regional reference frames and single-receiver phase-ambiguity-fixed Precise Point Positioning techniques. The stable Gulf of Mexico Reference Frame 2020 (GOM20) provides a robust and consistent reference system for long-term offshore SHM in the Gulf of Mexico (GOM). Continuous GNSS observations (DEV1, 2010–2020) on a fixed platform in the Eugene Island 330 oil field are used to illustrate the methodology. The platform was installed in 1982 in 82-m water about 130 km away from the Mississippi Delta coastline. The major monitoring items include horizontal movements, seafloor subsidence, structure submergence, and seasonal oscillations. The stand-alone GNSS monitoring achieves 3- to 4-mm root-mean-square accuracy in the horizontal direction and 7 mm in the vertical direction for daily positions in the GOM region. According to this study, the GNSS antenna (DEV1) has moved 6 cm toward the northeast with respect to GOM20 since 2010; the ongoing structure submergence rate in the Eugene Island 330 oil field area is approximately 15 mm/year, a combination of seafloor subsidence (12 mm/year) and sea-level rise (2.6 mm/year) with respect to GOM20. The submergence in the future 40 years (2021–2060) would be greater than 0.6 m, likely between 0.8 and 1.0 m, but is unlikely to exceed 1.3 m. The peak-to-trough amplitudes of the seasonal movements at the top of the platform are below 5 mm in all three directions, comparable with the seasonal movements recorded by onshore GNSS in the Louisiana coastal region. The methodology introduced in this article can be applied to SHM in other offshore regions where stable regional reference frames are available. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is crucial for ensuring the safety and performance of offshore platforms. SHM uses advanced sensor systems to detect and respond to negative changes in structures, improving their reliability and extending their life cycle. Model updating methods are also useful for sensitivity analysis. It is feasible to discuss and introduce established techniques for detecting damage in structures by utilizing their mode shapes. In this research, by considering reducing the stiffness of elements in the damage scenarios, we conducted simulations of the models in MATLAB, including both two-dimensional and three-dimensional structures, to update the method suggested by Wang. Wang’s method was improved to produce a sensitivity equation for the damaged structures. The sensitivity equation solution using a subset of mode shapes data was found to evaluate structural parameter changes. Comparing the updated results for Wang’s method and the suggested method in the two- and three-dimensional frames showed a noticeable modification in damage recognition. Furthermore, the suggested method can update a model containing measurement errors. Since Wang’s damage detection formulation is suitable only for 2D structures, this modified framework provides a more accurate decision-making tool for damage detection of structures, regardless of whether a 2D or 3D formulation is used.

Structural health monitoring (SHM) is crucial for ensuring the safety and performance of offshore platforms. SHM uses advanced sensor systems to detect and respond to negative changes in structures, improving their reliability and extending their life cycle. Model updating methods are also useful for sensitivity analysis. It is feasible to discuss and introduce established techniques for detecting damage in structures by utilizing their mode shapes. In this research, by considering reducing the stiffness of elements in the damage scenarios, we conducted simulations of the models in MATLAB, including both two-dimensional and three-dimensional structures, to update the method suggested by Wang. Wang's method was improved to produce a sensitivity equation for the damaged structures. The sensitivity equation solution using a subset of mode shapes data was found to evaluate structural parameter changes. Comparing the updated results for Wang's method and the suggested method in the two- and three-dimensional frames showed a noticeable modification in damage recognition. Furthermore, the suggested method can update a model containing measurement errors. Since Wang's damage detection formulation is suitable only for 2D structures, this modified framework provides a more accurate decision-making tool for damage detection of structures, regardless of whether a 2D or 3D formulation is used. [ABSTRACT FROM AUTHOR]

Vibration sensing is of great significance in offshore engineering monitoring and safety detection. This paper presented a low-frequency vibration sensor (LV-TENG) based on a cantilever-beam-structured triboelectric nanogenerator, which can perform high-precision vibration sensing while conducting vibration energy collection effectively. The LV-TENG was composed of two aluminum electrode layers, a spring steel sheet covered with polytetrafluoroethylene (PTFE) and a first-order vibration mode structured frame. Under the excitation of external vibration, the spring steel sheet undergoes first-order modal vibrations between the aluminum electrodes and generates a periodically fluctuating electrical signal in the external circuit. The vibration profile of the cantilever beam was first analyzed theoretically to provide guidance for structural design. On this basis, the influence of the main structural parameters, including the structure of the Al electrode, the thickness of the steel plate, and the electronegative materials, on the output performance of LV-TENG was experimentally investigated and the structure was optimized to enhance electrical output. The results showed that the LV-TENG can accurately sense structure vibration with a frequency of 0.1 Hz to 5.0 Hz and an amplitude of 2.0 mm to 10.0 mm. The measured output voltage followed a positive linear relationship with frequency and the fitted correlation coefficient reached 0.994. The demonstration experiment indicated that the LV-TENG is expected to provide a new avenue for low-frequency vibration monitoring and can be used for structural health monitoring analysis in marine engineering. [ABSTRACT FROM AUTHOR]

Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem. First, the sensor subsystem is determined to include the structure information, component information, and vibration information monitoring of the offshore platform. Based on the monitoring target, three sensor types including incline sensor, acceleration sensor, and strain sensor are initially selected. Second, the assessment subsystem is determined to include safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation, and damage identification based on strain modal using strain monitoring. Overall performance evaluation based on frequency variation and damage identification based on Strain modal are illustrated. Finally, an offshore platform in the East China Sea is selected to establish a finite-element model to discuss the application and feasibility of the SHM system, the frequency variation due to scouring, corrosion, the growth of marine organisms, and temperature variation was investigated, and the overall performance of the platform was also evaluated. This work can provide a reference for installation and implementation of SHM system for offshore platform. [ABSTRACT FROM AUTHOR]

In structural engineering, offshore structures are undoubtedly among the most exposed to the effects of harsh environmental conditions. The external conditions of these semi-immersed systems involve complex combinations of wave and wind loads. The operating conditions are also unique because oil production platforms are subjected to repeated loading and unloading cycles of the extracted material, which continuously alter their mass. These characteristics make the definition of a structural health monitoring (SHM) protocol highly challenging but necessary to avoid environmental disasters. In this regard, this study discusses an SHM method that can be applied to offshore structures under realistic wave and wind loads. This approach combines anomaly detection, frequency domain decomposition, and a cointegration strategy. Two machine learning regression algorithms were tested to define a cointegration relationship: the support vector machine and the relevance vector machine. The effectiveness of the overall method was evaluated on time-domain signals generated from a finite-element model of a fixed steel platform, on which the Davenport and JONSWAP spectra were used to simulate wind and wave forces. The results show that this damage detection strategy is effective in supervising the health conditions in the analyzed scenario. • A Structural Health Monitoring (SHM) strategy is presented, based on Frequency Domain Decomposition and Cointegration. • Innovatively, the method is output-only, allowing its unrestricted use on-site and under normal operating conditions. • It can be applied directly to the time series as recorded from the structure's dynamic response. • Instead of the classic assumption of white Gaussian noise as input, realistic wind and wave forces are considered. • Different operating conditions and damage scenarios are numerically simulated and investigated. [ABSTRACT FROM AUTHOR]

In this paper, structural characteristics are evaluated by displacement and frequency indicators that indicate the real-time health status of offshore platforms. This paper uses an accelerometer to collect the dynamic response of the platform in the event of a ship collision. The main contributions of this research are reflected in three aspects. Firstly, based on Empirical Mode Decomposition (EMD) multiscale decomposition, the noise range is determined according to the scale and the average value of the standardized accumulation mode, and the original acceleration sequence is denoised. Secondly, two impact tests were carried out to understand the platform's structural characteristics under an external load. Combined with the FFT algorithm and Hilbert Huang transform, the three-dimensional information of the time, frequency, and energy is analyzed. Finally, a method of high-frequency dynamic displacement reconstruction is proposed. According to the extracted vibration frequency information, the parameters for the filter are reasonably set, and the denoised acceleration time sequence is processed with bandpass filtering and quadratic integration to obtain the high-frequency dynamic displacement of the structure. The results show that the high-frequency dynamic displacement of the accelerometer reconstruction is 1.5 mm. Two collision event frequencies, 1.477 Hz and 1.483 Hz, were successfully extracted from the north direction. [ABSTRACT FROM AUTHOR]

Offshore platform plays an important role in ocean strategy, and the construction of structural health monitoring (SHM) system could significantly improve the safety of the platform. In this paper, complete SHM system architecture design for offshore platform is presented, including the sensor subsystem, data reading and transferring subsystem, data administration subsystem, and assessment subsystem. First, the sensor subsystem is determined to include the structure information, component information, and vibration information monitoring of the offshore platform. Based on the monitoring target, three sensor types including incline sensor, acceleration sensor, and strain sensor are initially selected. Second, the assessment subsystem is determined to include safety monitoring and early warning evaluation using static measurements, overall performance evaluation based on frequency variation, and damage identification based on strain modal using strain monitoring. Overall performance evaluation based on frequency variation and damage identification based on Strain modal are illustrated. Finally, an offshore platform in the East China Sea is selected to establish a finite-element model to discuss the application and feasibility of the SHM system, the frequency variation due to scouring, corrosion, the growth of marine organisms, and temperature variation was investigated, and the overall performance of the platform was also evaluated. This work can provide a reference for installation and implementation of SHM system for offshore platform.

Structural health monitoring has become increasingly important in the effective evaluation of structural health conditions and the maintenance service of structures. The validity and convenience of obtaining sensor data are critical for data mining, feature extraction, and condition assessment. A fast wireless low-frequency vibration inspection system (FWLVIS) based on wireless sensor networks for offshore platform structural vibration inspection is presented in this paper. The designed system consists of intelligent acquisition equipment and eight wireless nodes with low-frequency acceleration sensors, while the entire system has 64 collection channels. The wireless nodes integrated with a vibration sensing unit, an embedded low-power micro-processing unit, a wireless transceiver unit, and a large-capacity power unit perform functions, which could perform data collection, initial analysis, data storage, and wireless transmission. The intelligent acquisition equipment integrated with a high-performance computation unit, a wireless transceiver unit, a mobile power source, and the embedded data analysis software could completely control the multi-wireless nodes, receive and analyze the data, and implement the parameter identification. Experiments are performed on a single pendulum, and then on an offshore platform model is constructed to verify the FWLVIS. The experimental results show that the system has the following characteristics: fast arrangement, high sampling rate, high resolution, and capacity for low-frequency inspection. Thus, the system has good application prospects and practical value in the field of structural health monitoring and non-destructive inspection. [ABSTRACT FROM AUTHOR]

The structural damage identification, which entails finding a best approximate solution to an over-determined system of linear equations, is actually one of the practical problems encountered in structural health monitoring. A major challenge to structural damage identification is the ill-conditioned problem caused by noise contamination and spatially incomplete measurements. A novel Tikhonov regularization iterative method (TRIM) is proposed to solve the ill-conditioned system of linear equations. This method iteratively reconstructs the regularization matrix by gradually updating the regularized solution. A merit is that the false-positive indicators of damage are greatly reduced; and as a result this approach would be able to detect smaller damages that could not be detected by using traditional approaches. Another development embedded in TRIM is that a procedure, called singular value dichotomy, is developed to determine the regularization parameters. Several problems, such as the tremendous prior trials, due to the application of L-curve, are avoided. A numerical study is conducted on an offshore platform structure to demonstrate the effectiveness of the proposed method. The result shows that the new method outperforms the traditional Tikhonov regularization method in damage identification. [ABSTRACT FROM AUTHOR]

The purpose of structural health monitoring (SHM) is to detect damage as early as possible. To this end, damage-sensitive features (DSFs) must be evaluated to achieve the objectives. Researchers have developed numerous features. However, some gaps remain in the literature for feature selection; thus, conducting research that collects, evaluates, and suggests suitable DSFs are deemed essential. Consequently, this article seeks to identify effective DSFs and damage diagnosis recommendations. Seven categories are examined for output-only DSFs: 1) statistical features, 2) spectral function features, 3) modal property-based features, 4) entropy-based features, 5) transmissibility function (TF), 6) time series models, and 7) vibration parameter features. The sensitivity of DSFs to noise is also investigated. Three types of offshore platforms subjected to ambient loads are modeled in SAP2000. For each model, nine damage scenarios with three types of damage (cross-section area, moment of inertia, and pinned connection) are considered. The results of this study include a comparison of the performance of categories and features, efficient features for identifying the damage occurrence, and efficient features for locating the damage in noise-free and noisy conditions. Finally, suggestions are made for effective damage diagnosis. • This research covers a significant gap in the SHM literature, particularly for offshore platforms. • A comprehensive analysis of seven categories of output-only damage-sensitive features (DSFs) for damage detection is presented. • Three types of damage are considered: reduction of the cross-section area, the moment of inertia, pinned connection with severity ranging from 5% to 50%, and ambient loads such as waves, currents, and winds. • Suggestions are made concerning sensitive and efficient DSFs for damage detection. In addition, the noise sensitivity of various categories and features for noise conditions is investigated. • Several recommendations and observations on damage diagnosis are presented. [ABSTRACT FROM AUTHOR]

• A recursive window-based Bayesian inference approach is formulated and validated. • It is implemented for joint input-parameter estimation of an offshore structure. • Strain time histories at hotspots are accurately predicted through virtual sensing. • The proposed method outperform modal expansion method for virtual sensing. This paper presents a recursive Bayesian inference framework for joint parameter-input identification, and virtual sensing for strain time history prediction of an offshore platform using sparse output-only measurements. The studied offshore platform, known as FINO3, is in the North Sea and is instrumented with a variety of sensors, including accelerometers and strain gauges. Offshore platforms are fatigue critical structures due to harsh marine environmental conditions and continuous cyclic wind and wave loads. Therefore, continuous monitoring of strain time histories at hotspot locations of offshore structures is important for reducing maintenance cost and avoiding unexpected failures. A windowed unscented Kalman filter (UKF) is employed to estimate an uncertain modeling parameter (foundation stiffness) and unknown input load time histories using output-only acceleration and strain measurements. The input loads are divided into overlapping windows, and windowed inputs and model parameters are combined as an augmented state vector in the UKF framework. Then strain time histories at critical locations are estimated through a virtual sensing strategy using the estimated input loads and model parameter. A traditional modal expansion approach combined with model updating is also implemented for the purpose of verification and comparison. The proposed method is first demonstrated through a numerical study using a finite element model of FINO3, where accurate model parameter and input estimations are obtained. Then the approach is further investigated using the actual measurements on FINO3. More accurate strain predictions are provided by the UKF than the modal expansion approach, which recommends the proposed UKF method for fatigue monitoring and input estimation. [ABSTRACT FROM AUTHOR]

• The low-rate collection method based on posture sensors saves computing resources compared with the high-rate collection method based on acceleration sensors, which is beneficial to the health monitoring of offshore platform structure. • The collection method based on low-rate attitude can solve the problems of limited space resources and long-term continuous monitoring of offshore platforms. • We verified by nonlinear system simulation that the system characteristic identification effects based on low rate posture data are not lower than that based on high rate acceleration data. • We verified by model experiment that the damage identification effects of the tilt data based on low rate posture are not lower than the damage identification effects of the acceleration data based on high rate. • We use the tilt data of the FPSO soft york single-point mooring system to verify the damage identification. In vibration-based structural health monitoring (SHM), if the data are collected at a higher sampling rate from sensors, a large amount of computing resources and space resources will be consumed. For offshore platforms with limited space resources, there are many difficulties in long-term continuous monitoring. Aiming at this problem, the influence of sampling rate on structural damage identification is studied by comparing and analyzing the acceleration and posture sensors commonly used in offshore platform monitoring. In the simulation of parameter change of nonlinear system, random decrements and their spectrum characteristics are extracted from acceleration and displacement data respectively under different sampling rates, and the characteristics are classified by support vector machine (SVM) to identify the rule of change. The research of model experiment data also shows that the low sampling rate data can realize the identification of the damage, and the recognition degree of the low sampling rate data based on posture is not lower than that of the acceleration. The method is applied to the monitoring data analysis of FPSO soft york single-point mooring system and the damage situation is identical with the actual situation. The results show that the structural damage can be reflected from the low sampling rate posture monitoring data. [ABSTRACT FROM AUTHOR]