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Silva-Campillo, A.; Pérez-Arribas, F.; Suárez-Bermejo, J.C. Monitoring Systems in Shipping and Offshore Industry. Encyclopedia. Available online: https://encyclopedia.pub/entry/41558 (accessed on 21 December 2024).
Silva-Campillo A, Pérez-Arribas F, Suárez-Bermejo JC. Monitoring Systems in Shipping and Offshore Industry. Encyclopedia. Available at: https://encyclopedia.pub/entry/41558. Accessed December 21, 2024.
Silva-Campillo, Arturo, Francisco Pérez-Arribas, Juan Carlos Suárez-Bermejo. "Monitoring Systems in Shipping and Offshore Industry" Encyclopedia, https://encyclopedia.pub/entry/41558 (accessed December 21, 2024).
Silva-Campillo, A., Pérez-Arribas, F., & Suárez-Bermejo, J.C. (2023, February 22). Monitoring Systems in Shipping and Offshore Industry. In Encyclopedia. https://encyclopedia.pub/entry/41558
Silva-Campillo, Arturo, et al. "Monitoring Systems in Shipping and Offshore Industry." Encyclopedia. Web. 22 February, 2023.
Monitoring Systems in Shipping and Offshore Industry
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This entry is adapted from the peer-reviewed paper 10.3390/s23042099

Maritime transport is the main mode of international trade (up to 80% of goods); thus, marine structures are critical because of its specific influence to trade. Ships and offshore structures are used worldwide for a variety of functions and in a variety of water depths and environments. Monitoring the health of marine structures plays a key role in reducing the risk of structural failure. The incorporation of a health-monitoring system makes it possible to optimize the design, operation and/or maintenance, moving from criteria based on experience or conservative estimates, to others that take advantage of source of information about real-time in-service behavior. Structural monitoring also makes it possible to reveal the start of damage in structures that would otherwise remain invisible until a catastrophic/unexpected manifestation of damage occurs, allowing for decisions such as decommissioning for unscheduled maintenance actions or continuing until the next scheduled maintenance.

marine structural health monitoring sensors

1. Ship Structures

Table 1 depicts the typical sensors located on board for the common commercial vessel types.
Table 1. Structural sensor for common commercial vessel types [1].
Direct Measurand Container Ship Bulk Carrier Oil Tanker Ro-Ro Ship LNG Passenger Ship
Vertical accelerations at bow Accelerometer (ACC) (*)
Transverse acceleration amidships ACC (*) - ACC (*) - ACC (*)
Ship motion (at center of gravity) Motion reference unit (MRU) (**) MRU (*) MRU (*) MRU (**) MRU (*) MRU (*)
Global longitudinal stress amidships (port and starboard side) LSBG (**)
Global longitudinal stress at quarter length fore and aft of midship (port or starboard side) LSBG (**) LSBG (*)
Local transverse stress at transverse deck strip amidships Short baseline (SG) (**) -
Global longitudinal stress below neutral axis amidships (port and starboard) LSBG (*) -
Double bottom bending stress SG (*) -
Lateral loads at bowflare or bottom near forward perpendicular (Slamming pressure) Pressure Transducer (PT)/SG (*)
Lateral loads at side shell (wave pressure) PT/SG (*)
Sloshing response of liquid in tanks - PT/SG (*) - PT/SG (*) -
(*) optional/recommended, (**) typically required.

1.1. Container Ship

Yu et al. [2] presented a full-scale measurement solution designed to monitor the ship motions, wave conditions and structural response on an 8063 TEU container ship. Nielsen et al. [3] obtained a calculation method for the prediction of fatigue damage ratio in hull structures considering whipping stresses, by means of full-scale wave-induced stress ranges in a container ship in which the related fatigue damage ratios are calculated. Chen et al. [4] investigated a new approach to calculate the directional wave spectra from the measured ship responses in different scenarios of a large 10,000 TEU container ship under the combination of different stress and movement sensors.
Chen et al. [5] extracted data from the full-scale measurements of a 14,000 TEU double bottom container ship, whose sea states are similar to those of numerical simulation among the existing time series, in order to analyze the correlation between the double bottom and hull girder bending, on wave and vibrational response. Miyashita et al. [6] presented full-scale calculations (longitudinal stresses) of an 8600 TEU container ship carried out during four years and two months. The longitudinal stresses measured through sensors located on the midship are divided into horizontal bending stresses, hull girder vertical bending stresses, axial stresses and warping stresses. Chen et al. [7] discussed an approach, based on wave spectra that are estimated from restricted measurement data, to reproduce unmeasured hull stress responses.

1.2. Bulk Carrier

Kim et al. [8] showed the typical general arrangement of a bulk carrier with the common location of sensors on this type of vessel.

1.3. Oil Tankers

Several monitoring installations were carried out on ships in 1990 [9] and on other ships between 1990 and 1991 [10]. This system consists of about six long deformation sensors, two pressure sensors, an accelerometer and motion sensors. Hu and Prusty [11] found that in order to obtain more data on the deformation of ship hulls, it is necessary to deploy more strain gauges on the side hulls and transverse bulkhead. In terms of the dynamic loads of the ship, it is necessary to install pressure sensors at different locations on the side hulls. Accelerometers need to be mounted in three directions to measure the pressure of cargo oil.

1.4. Others

Stull et al. [12] reported the results of an active research project aimed at employing pattern-based structural health monitoring approaches on emerging and current hull structures. Takaoka et al. [13] focused on the implementation of fatigue damage sensor (FDS) on FPSOs for fatigue life consumption. Zhang et al. [14] provided a statistical and artificial intelligence approach to conduct resilient condition monitoring applied to a hawsers system in a FPSO oil offloading system. Kaminski [15] described the data processing and interpretation tools to prevent the fatigue damage of FPSOs. Thomas et al. [16] studied the application of extended stress, motion and hull wave measurements, together with a sophisticated finite element model, under asymmetric loading case.
Sato [17] conducted a fatigue monitoring program on a 135,000 m3 LNG carrier consisting of a comprehensive range of measurement elements. Yamamoto et al. [18] distributed a compact fatigue damage sensor into LNG carrier that can identify accumulated fatigue damage in welded structures by mounting it to the component and inspecting it after a certain time. Drummen et al. [19] presented the setup of two types of hull-structure-monitoring (HSM) systems. One is an overall system focused on global load effects and strains. The other is a local system aimed at detecting cracks through acoustic emission (AE) monitoring. Long base strain gauges are an important part of the global HSM system that was installed on USCGC STRATTON. The location of these sensors was optimized using hydro-structural calculation tools [20].
Cusano et al. [21] described and compared both full-scale and model-test monitoring campaigns and the most significant collected data, referring to the occurrence of slamming events on the MDV−3000 mono-hull ro-ro passenger fast ferry, focusing on the longitudinal bending moment amidships. Jensen et al. [22] studied a fast patrol boat (KNM Skjold), a twin-hull surface effect ship (SES) made of fiber-reinforcement polymer (FRP) sandwich composites with 47 m long and 13.5 m wide through the Composite Hull Embedded Sensor System (CHESS) project, of which the main objective was to design, develop, fabricate and install strain-monitoring systems using distributed fiber optic sensors for hull structure monitoring. These contents presented a method for measuring the global loads based on extensive finite-element analyses and strain measurements from networks of FO strain Bragg sensors attached to the hull of KNM Skjold.
Torkildsen et al. [23] presented a thorough introduction to the ship hull structural health-monitoring system, with the analysis of the data recorded onboard the Royal Norwegian Navy (RNoN) Mine Counter Measure Vessel (MCMV) “HNoMS Otra”. The HSV-2 Swift (HSV 2) is a high-speed vessel (wave piercing catamaran) of the United States Navy. Sielski [24] studied this vessel for detecting damage in the structure and to extend this detection capability to a prognosis capability [25]. Another investigation that also deals with the HSV-2 vessel is the one proposed by Mondoro et al. [26], where they provided a method for predicting the responses of vessels in non-observed cells by integrating data from the observed limited number of cells.
Majewska et al. [27] presented and discussed experimental research on tall sailing ships using fiber Bragg grating (FBG) sensors for the foremast. They determined the stress/strain level of the foremast during her normal operation to establish the effectiveness, quantity and configuration of the sails for the strain/stress level of the foremast. Hageman et al. (2015) [28] conducted a hull fatigue monitoring system for FPSO by coupling a minimum sensor array with automated data planning processing to collect the hull response, fatigue loading and wave conditions encountered. Ferreira et al. (2017) [29] described a fiber optic strain gauge sensor-based monitoring system used to recreate in real time the form of a sail using an algorithm developed and integrated to convert strain readings into deformations.
Söder et al. [30] presented a method for monitoring the stresses in ro-ro vessels by real-time measurement of ship motions. Johnson et al. [31] proposed a wireless, rapidly deployable hull monitoring system with an associated analytical framework that employs hull measurements to evaluate the lifecycle performance of a ship.
Yan et al. [32] suggested a new technology for the monitoring of the structural health of the stinger of a large deep-water pipe-laying vessel. Roberts et al. [33] presented field tests on the 138 m passenger and vehicle ferry Smyril, operating in the North Atlantic Ocean, using GPS and FBG kinematic sensors. Hageman and Thompson [34] assessed, through a virtual monitoring technique, the structural behavior of a frigate ship. There is a multi-purpose cargo vessel (INF 2 classified) named Atlantic Osprey with four fatigue damage sensors on board [35].

2. Offshore Structures

The monitoring of offshore structures can be classified as metocean factors related with the environmental conditions (wind, waves, currents, ice, etc.) and structural operational status [36][37].

2.1. Tension Leg Platform (TLP)

Van Dijk and van de Boom [38] evaluated, by means of full-scale monitoring, the Marco Polo Tension Leg Platform under exposure to hurricane and loop-current conditions, under high- and low-frequency modes of motion, under the fatigue loading of the platform, and under the dynamic behavior of the tendons and risers with focus on the vortex-induced vibrations.

2.2. Wind Turbine

Mieloszyc and Ostachowicz [39] presented an application of the structural health monitoring (SHM) system based on FBG sensors dedicated to an offshore wind turbine support structure (tripod) model. Nejad and Moan [40] studied the health monitoring of a 5 MW spar wind turbine drivetrain by means of a decoupled analysis method. Kim et al. [41] suggested and tested a structural health-monitoring method for floating offshore wind turbines (FOWTs) by using modal analysis with signals from numerical sensors. Kou et al. [42] studied, for a semi-submersible platform under repair, eight old main supports that connect the columns to the pontoon in order to substitute them with new ones, by means of a structural stress monitoring of eight key points calibrated with finite element models.
Moreira and Guedes Soares [43] found a technique using artificial neural networks to estimate wave-induced vertical bending moment and shear force from ship movements to incorporate it into a hull monitoring system. Vidal et al. [44] proposed a methodology for the detection and localization of damage in a wind turbine with jacket foundations, using eight triaxial accelerometers to identify any anomaly in the structure’s dynamic behavior. Yang et al. [45] proposed an offshore wind turbine deck-structure-monitoring system consisting of vibration, deformation and corrosion.

2.3. Jacket Platform

Lotfollahi-Yaghin et al. [46] conducted a numerical investigation in a jacket offshore platform that operates in 70.2 m water depth in the Persian Gulf. Sun et al. [47] studied the dynamic response of a jacket offshore platform under a seismic excitation model based on the collection of data provided by the FBG sensors and strain gauges. Ge et al. [48] examined the stress response of the jacket legs by FBG sensors to determine the impact strength on the same. Ali et al. [49] provided an experimental approach using piezoelectric sensor detection and finite element analysis method for the analysis of fatigue cracks in three types of joints. Tang et al. [50] proposed the methods of structural monitoring and early warning status on the basis of the aged coating characteristics of offshore platforms. Liu et al. [51] detected damage to the structure of the jacket platform by analyzing the acoustic emissions.

2.4. Jack up Platform

Archer [52] studied the monitoring of cyclic loads on a leg of the jack-up platform Nengue Sika during a transport from Singapore to West Africa. Shabakhty et al. [53] showed, based on a crack propagation approach and achieved information from inspection, that the remaining fatigue reliability of jack-up structures could be determined and updated by using a Bayesian procedure in the duration of the service time.

2.5. Spar

The dynamic response of the Neptune Spar platform was monitored under hurricane sea conditions [54]. Thethi et al. [55] presented a riser-monitoring strategy and implementation on a deepwater Gulf of Mexico Spar top tensioned riser. Karayaka et al. [56] installed riser and flowline monitoring (RFM) on Chevron Tahity Spar to study the dynamic response of the catenary [57].

References

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Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Arturo Silva-Campillo
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Juan Carlos Suárez-Bermejo
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