17 Fiber Optic Sensors

Woodhead Publishing Limited, 2013

434

17 Fiber optic sensors for subsea

structural health monitoring

B. GLISIC, Princeton University, USA

DOI : 10.1533/9780857093523.3.434

Abstract : Subsea structures operate in very harsh conditions and their operation and lifetime may be compromised by natural or manmade hazards and accelerated aging processes. Access to these structures for maintenance or repair purposes is diffi cult, expensive and requires trained personnel and sophisticated equipment. Structural Health Monitoring is a process that can provide important information about structural conditions and help increase safety and optimize maintenance and repair activities. Fiber optic technolog y has enabled reliable, stable, and robust sensors that can operate in subsea environment in long terms. This chapter presents the main fi ber optic sensors (FOS) principles, the methods of their implementation, and their potential in subsea applications.

Key words : structural health monitoring, integrity monitoring, subsea structures, fi ber optic sensors, long-gauge and distributed sensors.

17.1 Introduction

The increasing need for energy resources, expanded energy and information transmission networks, various researches, and even entertainment, have led to the construction of a range of subsea structures. Typical examples are risers, pipelines, rigs, power transmission and telecommunication cables, compression stations, submarine and remote operated vehicles, and even, recently, hotels. These structures operate in very harsh conditions low tem-perature, high pressure, and saline water and their operations and lifetimes may be compromised by natural or manmade hazards and accelerated aging processes. The failure of subsea structures may have very adverse conse-quences, such as injury and loss of life, economic losses, and harmful societal and ecological impacts.

Access to subsea structures for maintenance or repair purposes is diffi -cult, expensive, and requires trained personnel and sophisticated equipment. Structural Health Monitoring (SHM) has the potential to provide impor-tant information about structural conditions and help to increase safety and optimize maintenance and repair activities in an informed manner. Recent

Fiber optic sensors for subsea structural health monitoring 435


developments in fi ber optic technologies have provided reliable, stable, and robust sensors that can operate in harsh subsea environment for long terms.

This chapter presents the FOS for SHM. First, an introduction to the SHM process is provided, including aims, benefi ts, the main activities, and parties involved. Then, a section is included on FOSs, presenting their general prop-erties, functional principles, and specifi cations. Long-gauge and distributed sensors are recognized as particularly benefi cial, and the monitoring meth-ods for global structural and integrity monitoring are presented in detail in the following section. An illustrative example is given in this section. The challenges of implementation in subsea structures are presented and illus-trated with another example. Finally, the chapter fi nishes with thoughts on future trends and a suggested list of relevant literature.

17.2 Structural health monitoring

Civil and industrial structures and infrastructures, including subsea struc-tures, represent an important asset of every society. They generate or enable activities that contribute signifi cantly to wealth and societal welfare in gen-eral. Therefore, sustainable preservation of existing, and sustainable con-struction of new, structures and infrastructure are goals that are essential for the future vitality of the economy and prosperity of a society. The safest and most durable structures are those that are properly managed, and SHM plays an important role in these activities (e.g. Andersen and Fustinoni, 2006; Karbhari and Ansari, 2009; Wenzel, 2009, etc.). The information obtained from SHM helps increase structural and user safety, plan timely mainte-nance and preservation activities, verify hypotheses and reduce uncertainty on real structural behaviors and conditions, and widen knowledge regarding the structure being monitored. SHM helps prevent the adverse impacts that may occur in the case of structural defi ciency (social, economic, ecological, and aesthetic), and is critical to the emergence of sustainable civil and envi-ronmental engineering.

SHM is a process aimed at providing accurate and timely information concerning structural health condition and performance (Glisic and Inaudi, 2007). It consists of implementing a damage identifi cation strategy for struc-tures and infrastructure (Worden et al ., 2007). In a broader sense, SHM also includes structural identifi cation (Aktan et al ., 1997) and performance mon-itoring (Goulet et al ., 2010). To achieve the aims, SHM should (e.g. Worden et al ., 2007; Glisic et al ., 2010, etc.):

1. Detect unusual structural behavior (damage, deterioration, malfunction); 2. Register the time of unusual structural behavior occurrence; 3. Indicate physical location of the structure (e.g. structural member)

exhibiting unusual behavior;

436 Subsea optics and imaging


4. Quantify or rate the extent and/or magnitude of the unusual behavior detected;

5. Execute actions based on SHM input.

The SHM process includes recording over time (permanently, continuously, or periodically, in the short, mid, or long term) the parameters that in the best manner refl ect structural behaviors and transforming (by analysis) the recorded data into information about structural health conditions and/or performance (Glisic and Inaudi, 2007). Consequently, a monitoring system has three subsystems (e.g. Andersen and Fustinoni, 2006; Wenzel, 2009): a measurement subsystem, a data management subsystem, and a data analy-sis subsystem. The measurement subsystem consists of sensors, a reading unit (or readout unit or interrogator), and accessories (cables, protection boxes, etc.). The data management subsystem consists of hardware and software that provides data transmission (from the reading unit, either wired or wireless), storage, accessibility, and presentation. Finally, the data analysis subsystem consists of hardware and software that transforms the data into information about structural health conditions or performance. The data analysis is built on various pattern-recognition algorithms that can be model-based (e.g. Andersen and Fustinoni, 2006; Glisic and Inaudi, 2007; Wenzel, 2009) and/or model-free, i.e., data-driven (e.g. Sohn and Farrar, 2001; Posenato et al ., 2008). The most frequently monitored param-eters are summarized in Table 17.1. The selection of parameters to be mon-itored depends on several factors, such as the purpose and the nature of a structure, construction material, projected loads, environmental conditions, exposure to natural hazards, anticipated degradation phenomena, etc.

In order to implement SHM it is important to understand the SHM process. The main activities of the SHM process are: (1) selection of the SHM strategy, (2) installation of the SHM system, (3) maintenance of the SHM system, (4) data management and analysis, and (5) closing activities (in the case of inter-ruption of monitoring) (Glisic and Inaudi, 2003). Each of these activities can be further subdivided, as presented in Table 17. 2 (Glisic and Inaudi, 2007).

Each of the main activities is very important; however, it is crucial to create and properly implement an appropriate monitoring strategy. The

Table 17.1 The most frequently monitored parameters

Mechanical Strain, deformation, displacement, cracks opening, acceleration, vibration, load, etc.

Physical Temperature, humidity, pore pressure, etc. Chemical Chloride penetration, sulfate penetration, pH, carbonatation

penetration, corrosion, etc. Other Environmental conditions (wind, rain, icing, etc.), seismic activity,

etc.

W

oodhead Publishing Lim

ited, 2013

Table 17.2 Breakdown structure of the core monitoring activities (Glisic and Inaudi, 2007)

SHM strategy Installation of SHM system

Maintenance of SHM system

Data management and analysis

Closing activities

Monitoring aim Installation of sensors Providing for electrical supply

Execution of measurements (reading of sensors)

Interruption of SHM

Selection of monitored parameters

Installation of accessories (connection boxes, extension cables, etc.)

Providing for communication lines (wired or wireless)

Storage of data (local or remote)

Dismantling of SHM system

Selection of SHM system

Installation of reading units

Implementation of maintenance plans for different devices

Providing for access to data

Storage of SHM system components

Design of sensor network

Installation of software Repairs and replacements

Visualization

Schedule of monitoring Interfacing with users Export of data Data exploitation plan Interpretation Cost analysis Data analysis

The use of data



monitoring strategy infl uences, and is infl uenced by, each of the other SHM activities, and errors made during the planning and implementation stages are frequently diffi cult to cancel (Glisic and Inaudi, 2007).

The parties involved in a realization of an SHM project are: (1) the mon-itoring authority; (2) the consultant; (3) the monitoring companies; and (4) the contractors (Glisic and Inaudi, 2007). These entities must collaborate closely with each other, and they need not necessarily to be different; for example, a monitoring authority can also have the role of consultant or con-tractor. The relationship between the main SHM activities and the involved parties is presented in Fig. 17.1 (Glisic et al ., 2010).

From experience, the majority of the errors in realization of SHM occur because of poor understanding of monitoring activities and their mutual interactions, and incorrect distribution of tasks among the involved parties. When deciding about SHM, it is very important to study and understand SHM process in order to overcome various issues in planning and realiza-tion of SHM projects.

17.3 Fiber optic sensors for structural health monitoring

Fiber optic sensors (FOS) became commercially available since 1990s. They feature high sensitivity and precision, long-term stability, robustness against environmental interferences and reliability in harsh environments. An over-view on commercially available FOS and their basic physical principles is given in this section.

Selection of monitoring strategy

SHM processDecides

Installation of monitoring system

Data management

Monitoring authority

Monitoring company

Contractor

Consultant

Maintenance

Ending activities

Benefits

Propose Can have a role

Can have a role

Can have a role

Superv.Analys.

Delivers

Can have a role

Carriesout

17.1 The relationship between the main SHM activities and the involved parties (Glisic et al ., 2010).



17.3.1 Overview of ber optic sensors

A commercially available optical fi ber has a diameter similar to that of a human hair and consists of three components: core, cladding, and coating. The core is made of fused silica, and it guides the light along the length of the optical fi ber. In general, two types of fi ber are available: single-mode and multimode. The core of the single-mode fi ber is typically ranged between 5 and 10 m, while the core of the multimode fi ber is larger, typically in range of 50 m. The operating wavelengths of light for transmission in single-mode fi bers with minimal losses are 1310 or 1550 nm, while in multimode fi bers they are 850 or 1300 nm. The core is surrounded by cladding, which is also made of silica, but with a lower index of refraction than the core. The purpose of the cladding is to keep the light within the core, minimize losses, and physi-cally support the core. The outer diameter of the cladding is 125 m. Finally, a coating or jacketing surrounds the cladding and serves to protect the optical fi ber and to provide it with physical robustness so that it can be handled with-out risk of breakage. The coating is most commonly made of acrylate (outer diameter of 250 m), but for sensing purposes polyimide (outer diameter of 145 m) may be preferable, as it better adheres to the cladding. Typical com-ponents and dimensions of an optical fi ber are given in Fig. 17.2.

Apart from classical applications in the telecommunications industry, optical fi bers have also applied in bio-medical environments, and for sens-ing and monitoring. The high performance of FOS is inherently linked to its nature. The silica, main component of the core and cladding, is an inert material, resistant to most chemicals over a wide range of temperatures, and is therefore suitable for applications in harsh surroundings (Udd, 2006). Various packagings, particularly designed for fi eld applications, have made FOS robust and safe to deploy and operate in very challenging environments, such as civil engineering sites or subsea applications (Udd, 2006;Glisic and Inaudi, 2007). The light used for sensing purposes in the optical fi ber is not affected by nearby electromagnetic (EM) fi elds. Consequently, the FOSs are intrinsically immune to EM interference (EMI). This, along with the chemical indifference of silica, contributes signifi cantly to the long-term sta-bility and reliability of the FOS. The optical fi bers can have both a sensing

Coating, typ. OD = 250 m(0.0570.0098 in.)

Cladding, typ. OD = 125 m(0.0049 in.)

Core, typ. OD = 550 m(0.00020.0020 in.)

17.2 Typical components and dimensions of an optical ber (Glisic, 2009).



and signal transmission role. The capability to measure over long distances, exceeding several kilometers, without the need for electrically active compo-nents is an important quality when monitoring large and remote structures, such as long-span bridges, tunnels, and dams (Udd, 2006), but also pipelines (Inaudi and Glisic, 2010) and subsea structures (Inaudi et al ., 2007). In addi-tion, the passive optical signal makes them safe for use in the oil and gas industry, especially in downhole and well operation monitoring, as they can-not produce a spark in the event of malfunctioning. By appropriate packag-ing of a FOS, the propagating light can be made sensitive to a large variety of parameters (e.g. strain, inclination, acceleration, movement, temperature, humidity, corrosion, fl ow rate, etc.); therefore, multiple parameters can be monitored and combined in the same network (e.g. Measures, 2001).

As the area of FOS is very broad, it is not possible to present in detail all the sensing principles and all the types of available sensors. Instead, an overview is presented on strain and/or temperature sensors, as these FOS are generally proven in the area of civil engineering, and were either used in subsea applications to the certain extent (e.g. Roberts, 2007; Schlumberger, 2012; Weatherford, 2012), or have strong potential for use in this type of application. Most performing FOS principles, leading to successful large-scale fi eld applications, are for discrete sensor extrinsic interferometry, intrinsic interferometry, and fi ber Bragg gratings, while the distributed sen-sors are based on various light scattering effects in optical fi bers. Clear and detailed descriptions over different fi ber optic technologies can also be seen in Measures (2001) and Udd (2006). These books are highly relevant, and are a perfect introduction to FOSs, interrogators and related aspects.

17.3.2 Extrinsic FabryPerot interferometric (EFPI) sensors

The EFPI sensor measures dimensional change of a physical cavity, as shown in Fig. 17.3.

Few mmPartially reflecting

Lead fiber Targ

Partially reflectingPackaging

Incident light

1st partial reflection

(L)2nd reflection and re-coupling

17.3 Schematic representation of functioning principle of EFPI sensor (Glisic, 2009).



The sensor set-up consists of two multimode fi bers, the lead fi ber and the target fi ber, protected by packaging that also guarantees the strain trans-fer from the structure to the sensor. The fi bers are cleaved at 90 and their surfaces are partially refl ective, and separated by an air gap in the range of millimeters. The broadband light is sent from the source (placed in the read-ing unit) and travels through the lead fi ber until it reaches the cleaved end of the fi ber. Part of the light is then refl ected back to the reading unit, while the rest continues to travel through the air gap until it reaches the target fi ber, refl ects off its surface, and re-enters the lead fi ber. Two refl ected lights are then combined within the lead fi ber into an optical signal (construc-tive or de-constructive interference) that contains the information about the size of the air gap (Krohn, 2000), which is in turn decoded in the reading unit. Deformation of the structure results in a change in size of the air gap sensed by optical signal and determined in the reading unit. The air gap (and thus the EPFI principle) is in general insensitive to temperature change, but sensitivity to temperature is frequently present in sensors due to thermal expansion of the mechanical components of the packaging. The EFPI sen-sors face some challenges in large-scale applications, related to the remote position of the reading unit and multiplexing. Nevertheless, the complete SHM system is less expensive than any other FOS system when applied in small- to mid-scale automated SHM projects and even for large-scale SHM projects involving manual periodic measurements. The best performances of EFPI sensors are shown in Table 17.3.

17.3.3 Intrinsic (Sagnac, Michelson and Mach-Zehnder) interferometric sensors

Intrinsic interferometry uses the optical fi ber as the sensing body (as opposed to the air gap in EFPI). Commercially available sensors are based on Sagnac, Michelson, and Mach-Zehnder interferometers.

In the Sagnac interferometer two counter propagating light beams travel in opposite directions inside a rotating coil of fi ber. Since the optical path is identical and closed for both beams, the beam traveling against the rota-tion experiences a slightly shorter path than the other beam, which causes interference patterns when the light comes back to the insertion point. The position of the interference fringes depends on the angular velocity of the rotating coil, and this is called the Sagnac effect (Stedman, 1997). The Sagnac interferometer is used to create a fi ber optic gyroscope (FOG), which is principally used for rotation rate measurements and orientation in aerospace engineering. This sensing principle can also be used in dynamic measurements, but is not well known in commercial civil engineering applications.



In the Michelson and Mach-Zehnder techniques, two light beams are inserted in two single-mode optical fi bers and then the length difference in the fi bers is measured through a relative phase shift between the light beams. The fi bers in the sensor based on the Michelson interferometer have chemi-cal mirrors at their extremities that refl ect the light back to the photo detector placed in the reading unit. The sensor based on the Mach-Zehnder interfer-ometer has, in general, two outputs instead of the mirrors and the light signal is decoded at outputs by a photo detector. The Michelson interferometer per-forms very stable, long-term static measurements, while the Mach-Zehnder interferometer performs extremely sensitive short-term dynamic measure-ments. A commercially available sensor based on the Michelson interfer-ometer called SOFO (French acronym for structural monitoring using FOS, Inaudi 1997) has been proven in a large number of projects (Glisic et al ., 2010). The standard SOFO sensor is schematically represented in Fig. 17.4.

The active zone of the sensor measures the average strain between two anchor pieces. It consists of a protection tube with two optical fi bers, called respectively the measurement and reference fi ber, with mirrors silvered at their extremities. The role of the anchor pieces is to attach the sensor to the

Table 17.3 Best performances of the most successful commercially available discrete sensors (Glisic, 2009)

Fabry-Perot (EFPI) Interferometric (SOFO)

Fiber Bragg Gratings (FBG)

Gage length 5170 mm 250 mm20 m 10 mm2 m Multiplexing Parallel Parallel In-line and

parallel Max. number

of sensors in the network

32 Static: unlimited Dynamic: 8

16 channels 510 sensors/ch.

Stability Long-term Static: long-term Long-term Resolution 0.01% full-scale Static: 2 m

Dynamic: 10 nm 0.2

Repeatability (precision)

N/A Static:



monitored structure and to transmit the deformation from the structure to the measurement fi ber. The measurement fi ber is pre-tensioned between the anchor pieces so it can measure both the shortening and the elongation. The reference fi ber is loose in the protective tube, and thus independent of the deformation of the structure. Its purpose is to compensate the sen-sors for temperature changes. The passive zone serves as a carrier of optical signals from the reading unit to the sensor and back. It consists of one opti-cal fi ber, connector and coupler, all protected by a plastic tube. The light is sent from the reading unit through the passive-zone fi ber and is split by the coupler and inserted into the two fi bers of the active zone. The light travels to the extremities and refl ects back off the mirrors. Since the measurement and reference fi bers have in general different lengths, a shift in phase is cre-ated between the two refl ected lights, and this shift in phase is proportional to the difference in length of the two optical fi bers. Hence, the deformation of monitored structure changes the length of the measurement fi ber while the length of the reference fi ber remains unchanged. The change in length of measurement fi ber will induce a change in phase shift of the refl ected lights, which is decoded in the reading unit, using an integrated Michelson interferometer. The SOFO sensor is compensated for temperature and it is a true long-gauge sensor, as the light integrates, the strain along its gauge length. The typical gauge length of the SOFO sensor ranges between 20 cm and 10 m. The main challenge for SOFO technology is static and dynamic reading of sensors using the same reading unit. The best performances are presented in Table 17.3.

17.3.4 Fiber Bragg-grating (FBG) sensors

FBG sensors are the most popular in large-scale applications, as they allow for both static and dynamic monitoring of strain and temperature. The FBG sensing element consists of periodical changes created in the core of single-mode optical fi ber by appropriate exposure to ultraviolet light (Kang et al .,

3

32

2

4

4

4

4 5

1An

chor

Anch

or

MirrorsCoupler

L0

(L0 + Ls)

Ls

17.4 Schematic representation of functioning principle of SOFO sensor (Glisic, 2009).



2007). The FBG behaves as a partial mirror: the light containing a range of wavelengths will mostly pass through the FBG; however, one specifi c wave-length will refl ect back, as shown in Fig. 17.5.

The refl ected wavelength depends on the initial optical properties of the FBG that were embedded by the manufacturing process. In addi-tion, the optical properties of the FBG depend linearly on the strain and the temperature. Change in one or both of these parameters will change the wavelength that is refl ected back to the reading unit. By determin-ing the difference from the initial wavelength, it is possible to determine the strain or the temperature in the FBG. As the change in the refl ected wavelength depends simultaneously on strain and temperature, strain sensors must be compensated for temperature. The typical length of an FBG is several millimeters, and thus it is naturally a short-gauge sensor (fi ber optic equivalent for traditional resistive strain gauges). However, they can be packaged for use as long-gauge sensors (Li and Wu, 2005), e.g. by pre-tensioning of the FBG between two anchoring points.

An important advantage of FBG sensors is that several gratings with dif-ferent specifi c wavelengths can be placed along a single fi ber, allowing an easy in-line multiplexing. Thus, several sensors can be chained and read by a single scan from a single channel of the reading unit. For typical steel or concrete structures, with strain ranged between 5000 to +7500 microstrain, the number of sensors that can be chained in a single line varies between fi ve and ten. The best performances of the FBG sensors are presented in Table 17.3.

Presented discrete sensors can be used as sensing elements (transduc-ers) in appropriate packaging to create other types of sensors, such as fi ber optic accelerometers, tilt-meters, pressure sensors, fl owmeters, corrosion sensors, etc.

~10 mm

1(1, T1)

1(1, T1)

2(2, T2)

2(2, T2)

17.5 Schematic representation of principle of FBG sensors (Glisic, 2009).



17.3.5 Distributed sensors based on Rayleigh, Brillouin, and Raman scattered light

The distributed sensor is practically a cable that is sensitive at every point along its length, and consequently one distributed sensor is equivalent to a large number of discrete sensors. In addition, it requires single connection cable to transmit the optical signals from and to the reading unit, as opposed to a large number of connecting cables required in the case of wired discrete sensors. In the case of large structures, distributed sensors may be less dif-fi cult and more economical to install and operate than discrete sensors. An illustrative comparison between distributed and discrete sensors installa-tion is shown for a pipeline in Fig. 17.6.

The distributed sensor is sensitive to measured parameters (strain and/or temperature) at every point of its length, but it provides measurements at a large, yet fi nite, number of discrete points that are spaced along the sensor at a constant value, called the sampling interval. The measured parameter is not acquired in a single point, but it is actually averaged over a certain length, called the spatial resolution (Lanticq et al ., 2009). Thus, the spatial resolution of distributed sensors is equivalent to the gauge length of a discrete sen-sor (Glisic and Inaudi, 2007). Depending on the requirements of a specifi c application, the sampling interval and the spatial resolution are confi gured by the user. However, they are not independent from the other measure-ment settings. There is a strong correlation between sampling interval, spatial

Distributed sensors (sensing cables), singleenchained line sensitive in each point on cables

PipelineSeveral kilometers

Single connectingcable, simpleconnection

Up to few km

Readingunit

Readingunit

Large number ofconnecting cables

Discretepoint or long gage

Sensors

Complex connection

17.6 Schematic comparison between distributed vs. discrete monitoring (Glisic and Yao, 2012).



resolution, resolution and precision of measurement, and the acquisition time (time needed to perform one measurement), and frequently there is no an ideal combination of parameters, i.e., trade-offs must be made. For example, a measurement that is very precise and sensitive (small spatial resolution), is very slow, while a fast measurement affects one or both other parameters.

The three functional principles for distributed sensing are Rayleigh scat-tering (e.g. Posey et al ., 2000), Brillouin scattering (e.g. Karashima et al ., 1990), and Raman scattering (e.g. Kikuchi et al ., 1988). Each principle is based on change in the optical properties of the scattered light induced by change in magnitude of the measured parameters (e.g. strain and/or temper-ature). This is shown in Fig. 17.7.

Rayleigh scattering can be used for both strain and temperature monitor-ing, as it is based on shifts in the local Rayleigh backscatter pattern induced by the strain and/or the temperature changes. As in the case of FBG sen-sors, proper thermal compensation must be made to extract the pure strain measurements. Temperature measurements are performed with additional sensors containing a loose (strain-free) optical fi ber. The sensors based on the Rayleigh backscatter feature the highest resolution of measured param-eters and shortest spatial resolution compared with the other distributed techniques. However, the maximal length of the sensor is limited to only 70 m (Lanticq et al ., 2009). Consequently, this sensor is suitable for monitoring localized strain changes over relatively short distances. Best performances achievable in strain monitoring based on the Rayleigh scattering are pre-sented in Table 17.4.

The Brillouin-scattering sensing principle is based on the change in the frequency of Brillouin scattered light caused by strain or temperature. Thus,

Anti-stokes components

BrillouinRaman Raman

T

BrillouinT,

, T

o

, T

Wavelength

Stokes components

Rayleigh

17.7 Scattered light properties as encoding parameter for strain and/or temperature measurements. (Source: Inaudi and Glisic 2007, courtesy of SMARTEC SA, www.smartec.ch.)



similar to the Rayleigh-scattering-based sensors, the strain measurements must be compensated for temperature. Two forms of Brillouin scattering are used for sensing purposes: spontaneous (Wait and Hartog, 2001) and stimu-lated (Nikles et al ., 1996, 1997). Stimulated Brillouin scattering is less sensitive to the cumulated optical losses generated by manufacturing and installation of the sensing cable, and consequently allows for monitoring of exception-ally large lengths (Thevenaz et al ., 1999). For example, in the case of strain monitoring, a single reading unit with two channels can operate measure-ments over lengths of up to 10 km, while in the case of temperature moni-toring, lengths of up 50 km can be reached. Remote modules can be used to triple the monitoring lengths. Although the measurement specifi cations of Brillouin-based measurements are not as good as these of Rayleigh-based measurements (less precise, higher spatial resolution), the great advantage of Brillouin-based systems is the signifi cantly greater length of the sensor that can be read by a single scan (several kilometers). Thus, the Brillouin-based systems are particularly suitable for monitoring global strain changes over large distances. The state-of-the-art application in strain monitoring based stimulated Brillouin scattering is Gota Bridge, Gothenburg, Sweden, where fi ve kilometers of distributed strain sensors were installed (Glisic and Inaudi, 2012). The best performances in strain monitoring achievable with Brillouin-scattering-based systems are given in Table 17.4.

Raman scattering is sensitive to temperature only. The typical spatial reso-lution of the system is 1 m, and the typical sensor length ranges between few tens of meters and a few tens of kilometers. The advantage of the Raman-based temperature FOS vs the Brillouin-based one is in its insensitivity to strain: while for Brillouin-based temperature sensors the packaging must guarantee the strain-free state of the optical fi ber over large distances, this is not the case for Raman-based sensors. However, Brillouin-based tempera-ture sensors can cover several time longer distances . The comparison between Raman-based and Brillouin-based temperature sensors is given in Table 17.5.

Raman-scattering- and Brillouin-scattering-based sensors are largely used for leakage detection and localization in pipelines, industrial facilities, and dikes (e.g. Nikles et al ., 2004; Pandian et al ., 2009; Inaudi and Church, 2011), where the leakage is detected and localized as a change in temperature or in the thermal properties of the soil induced by leaked material. An example of successful detection of the leakage is shown in Fig. 17.8.

17.3.6 Remarks

In subsea structures, currently, the most represented FOS are distributed tem-perature sensors (e.g. Schlumberger, 2012; Weatherford, 2012) for measure-ments of temperature profi les (operational monitoring) and detection and localization of leakage (SHM and integrity monitoring). However, discrete



Table 17.5 The best performances achievable in temperature monitoring using distributed sensors (Glisic, 2009)

Brillouin (stimulated) Raman

Spatial resolution* 0.55 m 12 m Sampling interval* 100 mm 100 mm Max. no. of sensing

cables in network 16 16

Stability N/A N/A Resolution at 5 km

range* 0.1 C 0.3 C for 10 s

0.1 C for 1 min 0.05 C for 5 min

Resolution at 30 km range*

0.1 C 3.2 C for 15 min 1.5 C for 1 h

Error limit 1 C Depends on the length of sensors

Instrument range 200 C up to 500 C 25 C to +300 C Max. length of

sensor* 100 km 30 km

Strain sensitivity Sensitive, requires strain-free packaging

Insensitive, does not require strain-free packaging

Measurement speed* 20 s to 20 min 10 s to 1 h

* These parameters depend on each other. 1 = 1 m/m = 106 m/m.

Table 17.4 The best performances achievable in strain monitoring using distributed sensors (Glisic 2009)

Brillouin, stimulated

Brillouin, spontaneous

Rayleigh

Spatial resolution* 0.55 m 1 m 10 mm Sampling interval* 100 mm 50 mm 10 mm Max. no. of sensing

cables in network 16 N/A (1?) N/A (1?)

Stability N/A N/A N/A Resolution* 2 30 (accuracy,

2RMS) 1 1

Repeatability N/A



sensors are also applied, mainly temperature and pressure sensors, accel-erometers and fl owmeters (for operational monitoring, e.g. Weatherford, 2012) and, to a certain extent, strain sensors (for SHM, e.g. Roberts, 2007).

Operational monitoring and SHM of subsea structures are frequently offered as integrated solutions by large companies (e.g. Schlumberger, 2012; Weatherford, 2012). As the competition is fi erce, these companies do not disclose much information about their solutions, which is why it is diffi cult to get detailed data of practical applications.

17.4 Structural and integrity monitoring approaches using FOS

FOS offer unique tools for SHM long-gauge and truly distributed sensors that can be used for global structural and integrity monitoring. They both allow large coverage of the structure and consequently an improved perfor-mance in identifi cation of unusual structural behaviors. The particularities and application methods of these sensor types are presented in this section.

17.4.1 Division of FO strain sensors by gauge length

Materials fail at a point when the stress at that point exceeds the ultimate limit state. Strain is a parameter directly correlated to stress, and so any change in the stress fi eld is refl ected by a change in the strain fi eld. There

Temperature profile before the leakage

Station 30

Station 30

Station 31

Station 31

Leakage

Temperature profile when the leakage is detected

17.8 Example of successful leakage detection of a 55-km long buried brine pipeline. (Source: Nikles et al ., 2004, courtesy of Omnisens SA, www.omnisens.ch.)



is no effective means to monitor stress under real, on-site conditions; con-sequently, strain is one of the most important parameters to monitor (e.g. Widow, 1992; Chang, 2011; SHMII-5 2011). The fi rst signs of damage to a structure often have local character, and occur in the form of strain-fi eld anomalies. Typical examples are cracks and bowing in steel (which are early indicators of fatigue and loss of local stability), as well as non-structural cracks in concrete (which are early indicators of damage caused by over-loading, frost, alkali-reaction, or corrosion in reinforcing bars). Compared with conventional electrical strain sensors (resistive strain gauges and vibrat-ing wires), FOSs offer two new and unique sensing instruments: long-gauge strain sensors, and truly distributed strain and/or temperature sensors (see functioning principles in previous section). The former can be combined in topologies that allow for global structural monitoring, while latter allows for one-dimensional strain fi eld and integrity monitoring (Glisic and Inaudi, 2007). A classifi cation based on sensor gauge length and the functional prin-ciple is given in Fig. 17.9.

Using long-gauge and distributed sensors, it is possible to instrument large volumes of a structure with sensors, enabling its global monitoring. This represents a fundamental departure from standard practice based on the choice of a reduced number of points equipped with short-gauge sen-sors. A larger coverage provided by long-gauge and distributed sensors allows for monitoring of global structural behavior, but also considerably improves the sensitivity and reliability of damage detection by improving the chances for sensors to be in direct contact with the damage, as shown in Fig. 17.10.

Fiber optic sensors (FOS)

Short gauge sensors

Extrinsic fabry-perotinterferometry (EFPI)

{, T }

Michelson and machzehnder interferometry

(SOFO) { }

Brillouin scattering{, T }

Raman scattering {T }

Rayleigh scattering{, T }

Fiber bragg-gratingspectrometry (FBG)

{, T }

Fiber bragg-gratingspectrometry (FBG)

{, T }

Long gauge sensors Distributed sensors

17.9 Classi cation of FOS based on gauge length and functional principles. (Source: Enckel et al ., 2013, reprinted with permission of John Wiley and Son Ltd.)



The advantage of strain structural and integrity monitoring over tempera-ture integrity monitoring rests in the fact that strain variations may indi-cate generation of damage before it actually happens (e.g. fatigue can be assessed before the crack appears, excessive bending can be detected before the rupture of element, etc.), while temperature monitoring indicates the location of damage that has already occurred. Thus, strain monitoring can be used for preventive maintenance or repair actions, either during regular use of the subsea structures or after an accident or natural hazard exposure that did not crate visible damage on the structure.

17.4.2 Long-gauge sensors and structural monitoring

Construction materials and, in particular, concrete can be affected by local fl aws, such as cracks, air pockets, and inclusions. All these fl aws introduce dis-continuities in the mechanical properties of construction material observed at a meso-level. However, material properties observed at the macro-level are more indicative for assessment of global structural behavior (Glisic, 2011). For example, although reinforced concrete is highly inhomogeneous (con-sisting of hardened cement paste, aggregate, and reinforcing bars), reinforced concrete structures are in general analyzed at the macro-level as built of a virtually homogenous material cracked reinforced concrete. Consequently, for structural monitoring purposes it is necessary to use sensors that are sen-sitive to strain changes observable at macro-level, but in the same time insen-sitive to material discontinuities observed at meso-level. Hence, the primary interest is to evaluate the deformation of the resulting material as a whole,

C1

Damage A Damage B Damage C

Direct, reliabledetection

Short-gage

Long-gage

Distributed

Indirect orimpossible

Indirect orimpossibleIndirect orimpossible






A1 A2B1 B2C2

17.10 Schematic comparison between damage detection capabilities of short-gauge, long-gauge, and distributed sensors (Glisic and Adriaenssens, 2010).



rather than the exact strain in each individual component of the material. Global structural monitoring of structures made of inhomogeneous materi-als should be performed using long-gauge sensors. This is illustrated by the following simplifi ed example (Glisic and Inaudi, 2007).

Consider a reinforced concrete beam subjected to short-term bending, as shown in Fig. 17.11. The most relevant parameter to be monitored at the structural level is the curvature , as it is directly proportional to bending moment M ( M = EI , where E is Youngs modulus and I is moment of iner-tia of the cross-section). To monitor curvature, a minimum of two sensors should be used, installed at different distances from the centroid of the cross-section. For example, one sensor is installed at the top of the cross-section (index t in the fi gure) in the compressed zone, and the other sensor is installed at the bottom of the cross-section (index b in the fi gure), in the tensioned zone of the concrete. For homogeneous material, the relation between the measured strains t and b (at the top and at the bottom of the cross-section respectively), bending moment M , and curvature is given by

= = =

1r

MEI h

b t [17.1]

where h is the distance between the top and bottom sensors, and r is the radius of the curvature.

The two sensors used for monitoring curvature can be either long-gauge or short-gauge. Let 11 and 22 be two cross-sections instrumented with pairs of short-gauge sensors (denoted with g 1 and g 2 ) and let two parallel long-gauge sensors (notation s ) be installed on the top and bottom of the cross-section, as shown in Fig. 17.11. If the reinforced concrete element is loaded with a small load it will bend but no cracks will open in the tensioned zone. The strain fi eld in the beam will be quasi-homogeneous, with linear

Short-gauge sensorsM Radius of

curvature r

h

Stg1t

12

M

21Long-gauge

w

Cracks

Elastic line(after cracking)

g1bg2b

g2tSb

17.11 Comparison between long-gauge and short-gauge sensor on a bent concrete element. (Source: Glisic and Indaudi, 2007, courtesy of SMARTEC SA, www.smartec.ch.)



distribution across the cross-section and with constant values along the fi bers of material parallel to the centroid line (neutral axis). Consequently, Expression [17.1] can be applied to calculate curvature using measurements from either long-gauge or short-gauge sensors. However, if the load is increased suffi ciently to create cracks, the element will change the curva-ture, the crack will open (e.g. for w ), and the elastic line will move toward the compressed area of concrete, as the cross-sections are weakened. For the purposes of this study, let one crack occur exactly on the short-gauge sensor g 2 b , and let another crack occur so that the short-gauge sensor g 1 b is exactly in the middle between two cracks (see Fig. 17.11).

Three sensors installed in the compressed zone of concrete (short- and long-gauge included) are placed at the same distance from the centroid and, consequently, measure the same value of the strain, which is not the case with three sensors placed at the bottom of the cross-section. Sensor g 1 b measures a very small value (smaller than the ultimate extension strain of concrete), while sensor g 2 b measures an extremely high value (crack open-ing over gauge length); the long-gauge sensor s b measures a value of strain averaged over several areas of continuous concrete (between cracks) and crack opening, and thus evaluates the cracked reinforced concrete ele-ment as homogenous at macro-level. Expression [17.1] obviously cannot be applied to pairs of short-gauge sensors to calculate the curvature, as the results will signifi cantly depend on the location of the sensors. Nevertheless, the expression can be applied to the pair of parallel long-gauge sensors, and they will provide accurate and relevant information on structural behavior.

At the level of the entire structure, global structural monitoring can be performed by combining long-gauge sensors in suitable sensor networks designed based on structural analysis. The strategy is the following: fi rst the structure is divided into cells (Vurpillot, 1999), and then each cell is equipped with a combination of long-gauge sensors called the topology (Inaudi and Glisic, 2002), which best captures the expected strain fi eld in the cell; once the results are obtained from each cell, appropriate algorithms are used to retrieve the global structural behavior (Glisic and Inaudi, 2007). This method is schematically presented in Fig. 17.12. It has been success-fully applied to numerous structures worldwide, including pile foundations, buildings, and bridges, and has demonstrated excellent results as described in literature (Glisic and Inaudi, 2007).

17.4.3 Distributed sensors and integrity monitoring

Term integrity refers to the quality of being whole and complete, or the state of being unimpaired. Distributed sensing technologies based on FOSs offer unprecedented solutions for improved and reliable, yet affordable,



damage detection in large structures, i.e. for integrity monitoring. An impor-tant qualitative difference between the monitoring based on discrete and distributed sensing is: discrete sensors monitor strain or average strain at discrete, mutually distant points, and do not provide for coverage of the areas between the sensors, while the distributed sensors can be continuously installed along the structure, and provide one-dimensional (linear) strain-fi eld monitoring (see Fig. 17.6). Hence, each cross-section of the structure can be instrumented and the sensor is in direct contact with any damaged section, allowing very reliable direct damage detection. In this manner, integrity monitoring can practically be implemented, as illustrated by the following application (Glisic and Yao, 2012).

A 13-m long real-size concrete segmented pipeline was assembled in a large test basin fi lled with soil, and tested under simulated permanent ground displacement. The basin consisted of the movable and the fi xed part. The movable part was attached to four hydraulic actuators, which were used to introduce controlled displacement of the basin. The parallel topology of sensors was installed both on the pipe specimen and in the soil, parallel to the pipeline. It was then tested and its suitability for segmented pipe moni-toring was confi rmed by the test. The position of the sensors on the pipeline is shown in Fig. 17.13.

Cells subject to bending and big relative displacement,equipped with parallel and triangular topologies

Cells subject to bending only,equipped with parallel topology

Cells subject to bending and shear force,equipped with parallel and crossed topologies

Cells subject to bending and axial force,equipped with parallel topology

Cells subject to axial force only,equipped with simple topology

17.12 Schematic representation of global structural monitoring using long-gauge sensors (Glisic and Inaudi, 2007).



The buried concrete pipe equipped with distributed FOSs was exposed to relative shear movement of twelve 1-inch steps, between two parts of a testing basin, as shown in Fig. 17.14. The movement fi rst caused bending of the pipeline (shear displacements from 1 to 6 inches) and then the crushing of the joints (shear displacements of 7 inches), see Fig. 17.14.

The crushing of the joints was successfully detected and localized as a high strain change (big jump in strain value) at the location of the damage. Notice that at a distance of only 50 cm from the damage the sensors regis-tered only bending, but no damage. At the location of the damage, the sen-sors were in direct contact with the damage (direct damage detection) and the change in the signal due to damage was so high that the sensors would have successfully detected and localized the damage, even if the error limit

R, T 1, and L1 installed on pipeT 2, T 3, L2 and L3 embedded in soil

(a) (b)

T 3

Tape sensorTape2 sensorProfile sensorCord sensor

Soil

T 3

T 2

T 1L3 L2 L1

15 cm 20 cm

RPipe

8 20

.0 c

m =

1.6

0 mT 2

L3 L2 L1R

T 1

17.13 Topology of sensors used in validation test: (a) global view and (b) in the cross-section (Glisic and Yao, 2012).

Various distributed sensorsVarious distributed sensorsinstalled on pipeline under testinstalled on pipeline under test

Various distributed sensorsinstalled on pipeline under test

Tape sensor

Immobile part

1 2 3 4 5

50 Sensor Mobile part

Tape2 sensorCord sensor

Profile sensor

Crushedjoint

0.04

0.03

0.02

0.01

0.01

0.02

0.03

0.04

0.051 2 3 4

0.0008Tape sensor, L1

123456789101112Discretestrain 7

0.0006

0.0004

0.0002

0.0002

0.0004

0.0006

0.0008

0.001

0

Position (m)

Stra

in (

)5 6 7 8

0

Brillo

uin

frequ

ency

(GHz

)

17.14 View of sensors after the installation (before burying), the crushed pipe joint, and the strain measurement diagram showing damage detection and localization as extreme strain values (Glisic and Yao 2012). (Source: Source of discrete strain data: courtesy of Prof. Lynch group at University of Michigan, Ann Arbor.)



for the employed system (20 microstrain) was worse by an order of magni-tude. It is important to highlight that the increase in monitored strain due to bending was actually an early warning of changed structural condition of the pipeline, which can be used to prevent the damage before it occurs (e.g. by excavation and release of bending stresses).

The sensor embedded in the soil successfully detected ground displace-ment and localized the shear plane, as shown in Fig. 17.15. More details about this project can be found in Glisic and Yao (2012).

Distributed fi ber optic technologies have reached market maturity, and they have been applied in projects worldwide (e.g. Glisic and Inaudi, 2007; Bennett, 2008, etc.). Nevertheless, they have different properties from discrete sensors (sampling rate, spatial resolution, long measure-ment acquisition time), and their application and the interpretation of their measurements is not as straightforward as in the case of discrete strain sensors. Thus, a good familiarization with distributed sensing tech-niques is imperative to inform the decisions on their application in a spe-cifi c project.

17.5 Challenges related to subsea applications

Presence of water, high pressure, and corrosive agents are the inevitable com-ponents of the subsea environment that make the deployment and the use of SHM particularly challenging. Their infl uence to the components of SHM system and possible solutions are presented in more detail in this section, and illustrated through a development project for a deep sea application.

Fault-line

Joint #1

50

Movable north-end

Joint #2 Joint #3 Joint #4 Joint #5Pipe #5Pipe #4Pipe #3Pipe #2Pipe #1

3.40

m

Fixed south-end

1,400

Cordsensor

Profilesensor

1,2001,000

800600400

Ave

rage

stra

in (

)

200

200400600

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Joints1 inch2 inch3 inch4 inch5 inch6 inch8 inch9 inch10 inch11 inch12 inch

Position (m)

0

17.15 Distributed sensors buried next to the pipeline and sensor measurement in the soil with detected damage indicated by arrows (Glisic and Yao, 2012).



17.5.1 General considerations

On-shore structures and infrastructure are commonly inspected periodi-cally, and visual inspection represents the most frequent way of assessing the structures health condition. However, in the case of subsea applications, visual inspections are more diffi cult to perform. They require trained per-sonnel (divers), and their access to the structure may depend on weather conditions. In the case of subsea structures installed deep in the water, where even trained divers cannot reach, a remote operated vehicle (ROV) must be used. An alternative is a permanently installed system of cameras, but they may have limited vision due to reduced clearness of water and limited angle. Visual inspection, although very useful, cannot help identify minute defects that do not manifest on the structures visible surface. Thus, SHM is a needed and promising alternative, but installation of monitoring system is very challenging as the subsea environment is rather harsh. The monitoring system installed on a subsea structure is exposed to large number or poten-tial threats for its longevity. The main challenges can be sorted in three, mutually correlated, categories: physical (humidity, temperature), mechani-cal (pressure, impact), and chemical (corrosion, UV exposure, aging).

The fi rst basic challenge is water itself. Many engineering materials are sensitive in one way or another to humidity, e.g. dimensions change, mechan-ical properties change, etc. The temperature also affects a materials dimen-sional and mechanical properties. Thus, the underwater SHM components must have minimum sensitivity to humidity and temperature. For example, in the case of shallow water, the temperature changes may affect the mea-surements, while in the case of deep water the temperature may be constant, frequently close to 4 C, and the components of the monitoring system, built at room temperature, must be able to survive the thermal shock and low temperature in the long term.

Besides the humidity, the water exerts pressure on underwater SHM components. This pressure may vary due to underwater currents and tide. These pressure variations will cause dimensional changes, which may affect the SHM system components and its performance (e.g. accuracy, stability, etc.). In addition, the SHM components should be robust to high pressure in the long term, but also robust to potential underwater impacts and mechani-cal damages that can happen during, or be caused by, the normal operation of the structure, or due to falling objects and static and dynamic stresses generated by underwater currents, tide, or operational activities (e.g. vibra-tion during the exploration of oil and gas). As the data transmission is made using underwater cables, the connections of the SHM components (sensors) and the cables must be designed so the water penetration in the connection and in the cable is completely prevented. Penetrating water, especially in very high pressure environments such as oil and gas downhole installation,



will build-up pressure in the cable and may cause damage to the equipment on the surface end of the cable.

Finally, salt water is particularly corrosive, and thus all the components of the monitoring system installed underwater should be insensitive to cor-rosion. The chemicals content of the salt water may also cause the aging of SHM components, which can be accelerated in shallow waters by exposure to UV irradiation (e.g. the adhesives are particularly sensitive to aging).

From the above considerations, it is clear that the proper selection of materials used in SHM components, and especially the sensor packaging, are crucial for the success of the implementation of a subsea SHM sys-tem. Another critical challenge is the installation of the monitoring sys-tem itself. By preference, and if possible, the installation should be made above sea surface, i.e. in conditions where the installation sequence and quality can be controlled and validated. Underwater installations require trained personnel or the use of ROV, with very delicate installation proce-dures. The selection of the SHM system, its components, and development of installation procedures must be focused to long-term stable, reliable, and robust monitoring that is virtually maintenance free, as any replace-ment or repair of components may be diffi cult or impossible due to the underwater environment. Using FOSs is particularly suitable for a harsh subsea environment, as they are insensitive to environmental infl uences and have proven long-term stability. There are several companies that are currently offering integrated FOS solutions for SHM of subsea structures (e.g. Schlumberger, 2012; Weatherford, 2012, etc.), mostly based on dis-tributed temperature monitoring (see Section 17.3.6). The challenges and solutions for an SHM system for strain monitoring of subsea structures are illustrated by a project presented in the next subsection (Guaita et al ., 2004; Inaudi et al ., 2007).

17.5.2 Example of development of FO strain sensors for deepwater applications

The exploration and production of oil and gas from offshore fi elds is extend-ing to very deep waters. Recent plans and projects intend to build and oper-ate platforms above the water whose depth ranges between 1500 and 3000 m. Such applications impose high demands on the risers, which are the tubes that are used to perforate, extract, and export oil, and thus they connect the sea bottom to the platform. The risers are commonly built in steel and left to hang freely in the deep water. There are two main types of riser: vertical and catenary. Vertical risers connect the platform to the wellhead situated almost vertically below the platform, and they are used for drilling and pro-duction. Catenary risers are used for wells far from the platform, and they



vertically hung from the platform on one end and lie horizontally on the sea bottom on the other end.

An important issue in riser design and manufacturing is related to fatigue. For both vertical and catenary risers, the most fatigue-prone areas are the connection points between the riser and the platform at one end, and the connection to the wellhead (vertical risers) or the touchdown area (cate-nary risers) at the other end.

The evaluation of the fatigue performance of deepwater risers is com-monly addressed based on numerical simulation and indirect off-line mea-surements performed by accelerometers. These approaches are prone to error (since indirect), and delays (since off-line). Thus, an in-line permanent monitoring system is needed that directly measures the static and dynamic strain levels experienced by the riser in its most critical zones, throughout its lifetime. ENI E&P, SMARTEC and Tecnomare have developed and quali-fi ed a comprehensive solution for strain monitoring of risers based on long-gauge FOS (Guaita et al ., 2004; Inaudi et al ., 2007). The main components of

17.16 Sensors, watertight junction box with connector, individual sensor connections (lower end of the box) and deepwater optical cable (upper end of the box). (Source: Guaita et al ., 2004, courtesy of SMARTEC SA, www.smartec.ch.)



the system are the underwater sensing network, the interconnection system and the surface equipment.

The optical fi bers are packaged in a glass fi ber reinforced polymer profi le that contains both the bonded strain measurement fi ber and the free tem-perature reference fi ber. The selected packaging is very robust and provides for physical, mechanical, and chemical protection of the optical fi bers. The intrinsic interferometric FOS (SOFO) were selected for this application as they are self-compensated for temperature and allow for true long-gauge sensing with described packaging (internal bonding of the fi ber over entire length). The completed sensor is bonded and clamped to the riser surface (see Fig. 17.16). Riser cross-sections are equipped with four FO strain sen-sors, installed parallel to the riser axis and arranged at 90 angles around the riser circumference. The sensor gauge length can range between 0.5 and 5 m, and is typically 2 m. This sensor topology allows the evaluation of axial and bending strains in both orthogonal directions, and provides redundancy (i.e., the same information is available in the case of failure of one sensor).

A specially designed watertight junction box is placed next to the sensors, and the sensor signals are transferred to a standard underwater cable, which can be up to 5 km long. Each group of sensors, i.e. each measurement area, is connected through a separate cable. However, all the cables can be grouped in a bundle to facilitate installation along the riser during the deployment. Figure 17.16 shows sensors installed on the surface of the riser, the intercon-nection box, watertight connections between the individual sensors (lower end), and the multi-fi ber deepwater cable (upper end) (Guaita et al ., 2004; Inaudi et al ., 2007). The developed underwater equipment is designed to be installed in a riser manufacturing facility or on the platform, before the riser is placed in the water.

The surface equipment consists of SOFO static and dynamic reading units, the control PC, and associated data management software. It is located at a convenient location in the platform control room. The properties of the SOFO monitoring system are given in Table 17.3.

The proposed SHM system offers several benefi ts, especially in compar-ison with solutions based on traditional electrical sensors or other short-gauge sensors:

The use of long-gauge sensors does not require particular preparation of the surface and simplifi es the installation on the riser. Long gauge length is more suitable for structural monitoring (see Section 17.4.2) as it is insensitive to local changes in the riser material or coating mechanical properties, and thus the measurements are more representa-tive of the riser global behavior.



The system features very high strain resolution and dynamic range, which allows the use of the same sensing system for both large strain and small strain analysis. The SOFO sensors feature an excellent long-term stability and durability allowing the analysis of strain states during all phases of the risers life, from manufacturing to deployment and long-term use. Each sensor is individually connected to the surface equipment which offers an excellent level of redundancy. The sensors are based on FO technology and are therefore electrically passive, not requiring electrical connection, power supply, amplifi ers, or data acquisition systems (all opto-electronic components are installed at surface). The underwater equipment features long-term reliability, as it does not require maintenance, not having either moving parts or components subject to wear or corrosion.

The developed underwater sensing network was tested and qualifi ed through hyperbaric chamber tests, as well as through large-scale mechani-cal tests. The system has been qualifi ed for deepwater applications on a riser mock-up placed in hyperbaric chamber (up to 360 bar), as shown

17.17 Testing of underwater equipment in hyperbaric chamber. (Source: Guaita et al ., 2004, courtesy of SMARTEC SA, www.smartec.ch.)



17.18 Large-scale testing of sensors. (Source: Guaita et al ., 2004, courtesy of SMARTEC SA, www.smartec.ch.)

400

1200

Avera

ge s

train

()

11001000900800700600500400300200100

100 1 3 5 7 9 11 13 15 17Step of load ()

19 21 23 25 27 29 31

Test 1, comparison between measured and calculated values

0

Reduced scale test, strain and pressure, part 4

O P Q R ST

U V

Sensing tape 1Sensing tape 2Sensing tape 3Sensing tape 4

Sensing tape 1Sensing tape 2Sensing tape 3Sensing tape 4STape1-calcSTape2-calcSTape3-calcSTape4-calcDST1DST2DST3DST4

Pressure

300200100

100200

Date and time

Avera

ge s

train

()

pres

sure

(bars

)

300400

05.05.2003 09:36

05.05.2003 14:24

05.05.2003 19:12

06.05.2003 00:00

06.05.2003 04:48

06.05.2003 09:36

06.05.2003 14:24

06.05.2003 19:12

0

17.19 Results of hyperbaric cyclic test (top) and large-scale static test (bottom), con rming excellent performance of the monitoring system. (Source: Guaita et al ., 2004, courtesy of SMARTEC SA, www.smartec.ch.)



in Fig. 17.17. The hyperbaric chamber tests were designed to (1) evaluate the robustness of all sensing and interconnection components if exposed to the extreme pressures, (2) evaluate the robustness if exposed to pres-sure cycling, and (3) assess the accuracy of measurements under these conditions.

The full-scale tests aimed to assess the (1) global behavior of the sensors, as well as the behavior of each component, (2) quality of installation (adhe-sion + clamping) when exposed to large and cyclic forces, and (3) measure-ment properties of the sensors: accuracy, precision, and long-term stability. The tests were carried out on a 6 m long section of riser steel tube subjected to three-point bending (see Fig. 17.18). The test reproduced the maximum level of strain expected in real applications.

Both tests demonstrated the performance and reliability of the system and its suitability for the intended application. Examples of results are shown in Fig. 17.19.

17.5.3 Summary

The implementation of SHM in subsea applications is very challenging due to the harsh environmental conditions. The underwater part of the sys-tem has to satisfy the requirements of maintenance-free, long-term stabil-ity, reliability, and robust performance. Thus, particularities of physically, mechanically, and chemically challenging environment must be considered in developing such a system, and a thorough laboratory validation should be made before the system is deployed on a real subsea structure.

17.6 Future trends

Signifi cant developments in the telecommunications marketplace have led to reduction of the cost of the FOS, which is still high compared to tradi-tional electrical sensors. However, taking into account lifecycle costs of the structure, and associated costs for maintenance, repairs, and operation, the use of FOSs is actually affordable and justifi ed by superior long-term perfor-mance in very harsh conditions. That is why the research and development are continuing, existing FOS technologies are improving, and new products are appearing in the marketplace incessantly. Many doctoral theses, large numbers of journal and conference papers, and several books have been published about various aspects of FOS and SHM topics (see examples in the next section). Many companies working with FOS FOS manufactur-ers, system integrators, solution providers (see the next section) have been founded over the last two decades, and FOS technologies are expected to become one of mainstream tools for SHM of subsea structures.



The main physical principles of the functioning of FOS are now well-established, and for this reason research on the physical principles is not expected to dominate over the next decade; research on the enhancement of measurement and durability properties of various systems is expected to be predominant, and may require fundamental research; for example: creating large-scale multiplexing capability for EPFI sensors, single static and dynamic reading capability for SOFO sensors, FBG sensors that will self-decouple strain and temperature measurement, and the dynamic Brillouin-based dis-tributed sensor. In addition, there is the innovative use of optical fi bers for sensing purposes, such as guiding the laser wave for acoustic excitation (for wave propagation based damage detection, Lee et al ., 2012), distributed cor-rosion monitoring based on simple refl ectivity (Leung et al ., 2008), etc.

Another research area is in the application in SHM of subsea structures for parameters other than temperature and fl ow, which have become cus-tomary. For example, strain sensing and, in particular, using distributed FOS has enormous potential given its distributed sensing nature and large area coverage. Other examples are acceleration, corrosion, chemicals, and reac-tions, wave propagation and acoustics, position and movement with FOS gyroscopes, electrical supply characteristics, etc. However, this does not include only enabling the implementation of SHM based on FOS, but also research in the area of data analysis related to the specifi c monitoring sys-tem and the specifi c monitored structure.

17.20 Example of a control panel in a SCADA center. (Source: NTSB, 2011, courtesy of The National Transportation Safety Board).



Developments in the fi elds of informatics technology, telecommunica-tions, and computer science have led to the creation and implementation of various Supervisory Control and Data Acquisition (SCADA) systems, mainly used for continuous operational monitoring of such subsea struc-tures as pipelines (e.g. Reed et al ., 2004; NTSB, 2011). Currently, SCADA has mainly encompassed the processes (fl ow, pressure, etc.) and although it can be used to detect some types of damage (e.g. detection of leakage from the fl ow rate) it is not truly intended for SHM. Thus, in the future, the current SCADA infrastructure can be upgraded to accommodate the data management of SHM systems. Integration can be further leveraged by including Geographical Information System (GIS), in order to create a comprehensive system for data visualization, analysis, and damage detec-tion (Reed et al ., 2004). In correlation with data analysis presented in the previous paragraph, decision making tools, fed by the data from SHM sys-tem could be developed and integrated. An example of a control panel in a SCADA center is given in Fig. 17.20.

In conclusion, future trends in FOS for SHM include enhanced measure-ment capability and durability of FOS, development and implementation of sensors with superior performance for monitoring parameters that are not currently used for subsea monitoring, development of new monitoring solutions for specifi c subsea structures (including data analysis), and inte-gration in current SCADA systems. Progress in these areas has the potential to transform the health assessment of subsea structures: implementation of continuous on-line SHM promises to signifi cantly improve safety, but also to reduce the amount of periodical inspection required, consequently decreas-ing associated costs.

17.7 Sources of further information and advice

Clear and detailed descriptions of the different fi ber optic technologies can be found in the following books:

Measures, M.R. (2001). Structural Monitoring with Fibre Optic Technology . Academic Press, San Diego CA, USA. Udd, E. (2006). Fiber Optic Sensors: An Introduction for Engineers and Scientists . Wiley, New York NY, USA.

Development of fi ber optic methods for SHM based on short-gauge, long-gauge and distributed sensors can be found in the following books:

Glisic, B. and Inaudi, D. (2007). Fibre Optic Methods for Structural Health Monitoring , John Wiley & Sons, Inc., Chichester.



Karbhari, V.M. and Ansari F. (Eds.), (2009). Structural Health Monitoring of Civil Infrastructure Systems , Woodhead Publishing in Materials.

More information on new research and development on FOS, SHM, and emerging technologies in general can be found in papers published in spe-cialized journals and conferences. A non-exhaustive list of journals is:

Measurement Science and Technology Sensors and Actuators A: Physical IEEE Sensors Journal Journal of Lightwave Technology Structural Health Monitoring Journal of Civil Structural Health Monitoring Smart Materials and Structures

Recommended conferences are:

International Conference on Optical Fiber Sensors (OFS) SPIE Smart Structure/NDE International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII) International Workshop of Structural Health Monitoring (IWSHM)

Important information regarding FOS and SHM can be found on the web-sites of numerous organizations and companies, some of which are listed below.

FOS:

http://www.opticalfi bersensors.org/index/be-en/1697/ www.smartec.ch www.micronoptics.com www.omnisens.ch www.fi bresensing.com www.sensornet.com www.roctest.com www.ozoptics.com http://lunainc.com/

SHM of Subsea Structures

http://www.subsea.org/equipment/manufacturers+and+suppliers/instrumentation/listcat2.asp?cate=fi ber+optic http://subseaworldnews.com/



http://www.spe.org/index.php http://www.slb.com/services/completions/intelligent/wellwatcher/well- watcher_dts.aspx http://www.ep-weatherford.com/solutions/Optical_Sensors.htm www.ishmii.org

17.8 Acknowledgments

The author would like to express his gratitude to SMARTEC SA, Switzerland, Omnisens SA, Switzerland, and National Transportation Safety Board, USA, for allowing publication of their fi gures.

The pipeline test presented in Section 17.4 is based upon work sup-ported by the National Science Foundation under Grant No. 0936493. This research has been realized within the frame of George E. Brown, Jr. Network for Earthquake Engineering Simulation Research (NEESR) Program Solicitation NSF 09524. The method validation testing was per-formed at The Cornell Large-Scale Lifelines Testing Facility, the NEES site at Cornell University (Cornell NEES Site). Any opinions, fi ndings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily refl ect the views of the National Science Foundation.

SHM of Riser presented in Section 22.5 is based upon work performed at SMARTEC SA, Switzerland, in collaboration with Tecnomare, Venice, Italy and ENI E&P, San Donato, Italy.

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17 Fiber Optic Sensors