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doi:10.3723/ut.33.115 Underwater Technology, Vol. 33, No. 2, pp. 115–126, 2015 www.sut.org

AbstractA reliable mooring is the key requirement for offshore buoys involved in meteorological and tsunami monitoring missions. Mooring failure can lead to a loss of time-critical data which could have a serious impact on societal protection while, in addition, resulting in costly repair and reinstallation. Based on mooring component failures reported by the offshore industry, a structural reliability analysis was carried out on the mooring architecture for the instrumented data buoys used for monitoring Indian Seas. The results are compared with the field failure data obtained from annually-maintained operational moorings of the National Institute of Ocean Technology which have a cumulative in-place mooring dura-tion of approximately 0.76 million hours over a five-year period. The estimated mean time between failure (MTBF) was between 6.3 and 11.1 years, against the achieved field performance of 6.1 years with an average annual mooring availability of 99.7% with a seven-day mean time to repair (MTTR) period. The operating mooring integrity monitoring and corrective action system with a monitoring interval of three hours ensures that the moorings meet the required International Electrotechnical Commission (IEC) 61508 IL4 standards. The efforts undertaken in improving the mooring reliability and availability, based on lessons learnt from field failures and to meet Det Norske Veritas (DNV) position mooring standards, are also detailed. The analysis gives confidence on the reliability of the mooring system support for India’s ocean observation programme.

Keywords: instrumented buoy, mooring, reliability, tsunami, meteorology, oceanography

Reliability analysis and integrity management of instrumented buoy moorings for monitoring the Indian Seas

R Venkatesan, N Vedachalam*, P Murugesh, P Kaliyaperumal, CK Kalaivanan, T Gnanadhas and MA AtmanandNational Institute of Ocean Technology, Chennai, India

Received 15 December 2014; Accepted 22 July 2015

* Contact author. Email address: veda1973@gmail.com

Acronym list

ALS accidental limit stateAPI American Petroleum Institute

BoB Bay of BengalDNV Det Norske Veritas FIT failure-in-timeFMEA failure modes and effects analysisFMECA failure mode effect and criticality analysisGPS global positioning systemIEC International Electrotechnical CommissionIETWS Indian Early Tsunami Warning SystemINCOIS Indian National Centre for Ocean Information

ServicesMCC Mission Control Centre METOCEAN meteorological ocean MIMCAS mooring integrity management and corrective

action systemMTBF mean time between failureMTTF mean time to failureMTTR mean time to repair NCAOR National Centre for Antarctic and Ocean

ResearchNDBC National Data Buoy Centre NIOT National Institute of Ocean Technology NOAA National Oceanic and Atmospheric

AdministrationNSWC Naval Surface Warfare CenterOMNI ocean moored buoy network in the Indian

Ocean OOS ocean observation systemsOREDA offshore reliability data PFD probability of failure on demandPMEL Pacific Marine and Environmental LaboratoryPoF probability of failurePP polypropylene PTI proof test interval RoE return of experiencesSDRP standard deployment and retrieval proceduresSEM scanning electron microscopeSFF safe failure fractionSIF safety instrumented functionSIL safety integrity levelSWL safe working loadWMO World Meteorological Organization

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1. IntroductionA reliable mooring is essential for maintaining the position of offshore facilities such as ocean observa-tional platforms, vessels, floating renewable energy systems, and oil and gas infrastructure. Based on international statistics, more than 23 permanent moorings have failed since the year 2000 causing loss of life and damages to environment and prop-erty (Mousavi and Gardoni, 2014); hence, interna-tional attention has become focused on reducing future mooring failures (Maslin, 2013). Position keeping of ocean observational platforms, such as data buoys, is critical as they are used for societal protection such as cyclone tracking and for early warnings of a tsunami (Gasiewski et al., 2005; Venkatesan et al., 2013). Considering the conse-quences of mooring failures, and the trends in tech-nological change, regulatory agencies, such as Det Norske Veritas (DNV) and American Petroleum Institute (API), have set stringent reliability targets for positional moorings (Smith and Simpson, 2004; API, 2005; DNV, 2010). Developments in material technologies, increasingly sophisticated manufac-turing methods, improved quality standards, and enhanced modelling and simulation tools, are expected to help attain the target reliability levels. Further advances in monitoring and information technology raise the potential for monitoring and reporting the integrity of the moorings in real time (Meinig et al., 2005; Venkatesan et al., 2013).

The Bay of Bengal (BoB) and Arabian Sea basins are locations where a large proportion of the South Asian coast cyclones and coastal impacts have occurred. In the Indian Ocean there are two tsunami-genic zones: the Andaman-Sumatra trench, and the Makran coast. As a result, tsunamis are an ever-present threat to lives over the whole of India’s 7,500km-long coastline, where 30% of the national population reside (SrinivasaKumar et al., 2010; Ramadass et al., 2014).

The National Institute of Ocean Technology-Ocean Observation Systems (NIOT-OOS) was established in 1996 with the prime objectives of operating, maintaining and developing moored buoy observa-tional and related telecommunication networks in Indian waters. The 24:7 manned and automated Mission Control Centre (MCC) at the National Institute of Ocean Technology (NIOT) receives all the data from these moored buoys (ArulMuthiah et al., 2011; Sundar et al., 2013). Since 1997, over 550 moored buoy systems, ranging from coastal waters to deep oceans, have been deployed for col-lecting meteorological, water surface and subsurface parameters, as well as tsunami water level data. Fig 1 shows the range of coverage of deployment and operation of these buoys which range from 63°E to 93°E and 6°N to 20°N (Venkatesan et al., 2013).

The existing buoy systems have collected nearly five million datasets over 17 years with the main objective of recording extreme weather conditions.

Fig 1: Location of moored buoys in Indian waters

Longitude (deg)

Lat

itu

de

(deg

)

24

22

20

18

16

14

12

10

8

6

4

2

0

24

22

20

18

16

14

12

10

8

6

4

2

060 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96

60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96

TB12

AD06

AD07

Arabian Sea

Bay of Bengal

INDIA

TSUNAMI BUOY OMNI BUOY COASTAL BUOY MET BUOY

AD08

AD10

AD09

CALVAL

AD04

AD02CB04

CB05

BD11

BD13

BD14

BD12

BD10

BD09BD08

TB06

TB05

TB03

CB01

TB09

CB02

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Between 1997 to 2013, these moored platforms have detected 17 cyclones and detected changing water level records used to monitor tsunami waves (ArulMuthiah et al., 2011). The Phailin cyclone in 2013 was categorized at the Category 5 storm level and affected 12 million people. This prompted the evacuation of 550,000 people to safer locations which was India’s biggest evacuation in the past 23 years (The Times of India, 2013). Even though the losses caused by the cyclone were estimated to be about US $1 billion, advance warnings obtained from the ocean monitoring system data triggered the evacuation with a near-certain reduction in human causalities (Maslin, 2013). Considering the impor-tance of the mooring system, the present paper analyses its structural reliability, assesses the effec-tiveness of the implemented integrity management system and evaluates future targets in achieving reliable moorings.

2. Buoy mooring architectureThere are five types of moored buoy employed in the overall NIOT-OOS: the meteorological ocean (METOCEAN) buoy system measures and transmits meteorological data and sea-surface parameters; the ocean moored buoy network in the Indian Ocean (OMNI) measures and transmits meteorological data; surface and sub-surface buoy systems measure oceanographic parameters (up to 500m in depth); coastal buoy systems provide specific coastal obser-vations; and tsunami buoy systems detect and report of sea level changes during tsunami events. The architecture of a typical buoy mooring is shown in Fig 2.

The surface buoy is made typically of a polyure-thane foam-filled core encased by a fibre-reinforced plastic hull. It is moored to the seabed using a weight and anchor system arranged in an inverse catenary configuration using negatively buoyant wire rope, nylon rope and positively buoyant poly-propylene (PP) rope, with a series of suitable con-nectors, such as shackles and swivels. The chain attached to the weight and the PP rope is held in an upright position using a group of sub-surface floats. The swivels used in the interface portion of the rope and chains are used to prevent relative rota-tion between the interconnected systems. A combi-nation wire rope, which has PP sheathing on the metal rope, is used for connecting the surface buoy and the nylon rope so as to avoid failures owing to fish bites in the oxygen-rich subsurface region. The central cylinder portion of the surface buoy is used to mount the SIL4-configured lithium-based energy storage (Venkatesan et al., 2014) and the data acquisition systems, and is connected to externally

located sensors. The mast is equipped with sensors and antenna for transmitting the collected data to the MCC at NIOT through the Inmarsat satellite. The keel weight attached to the central cylinder is used to maintain the buoy stability, and the keel frame is used to house the subsurface sensors.

3. MethodologyStructural reliability analysis was carried out for the operating mooring architecture using mooring component reliability data based on offshore indus-try failure models such as those used by the Orissa Renewable Energy Development Agency (OREDA, 2009), and the Naval Surface Warfare Center (NSWC, 2011). The results obtained were compared with the field failure data recorded from the NIOT-OOS -operated moorings which have recorded approximately 0.76 million hours in a five-year period. For the achieved mooring reliability, and the mission objective, the required integrity level and the monitoring interval requirements were computed based on IEC 61508 HSE Safety Integrity Level (SIL) standards on safety reliability (IEC 61508, 2000). In order to achieve the required SIL level, it

Sensor arm

Upper mast

FRP hull

Instrument housing

Keel weight

Keel frame

Chain with shackle

Chain with shackle

Dead weight

Anchor

Combination rope

Sub-surface float

Nylon rope

Polypropylene rope

Fig 2: Architecture of the moored surface buoy system

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was necessary to employ an implemented mooring integrity management and corrective action system (MIMCAS); this is explained in detail below. The importance of improving the mooring reliability and the ongoing efforts to meet the stringent DNV standards are also detailed.

4. Mooring reliability analysis based on global failure data4.1. Global mooring components failure-in- time dataOffshore mooring reliability modeling methods are available for industry applications (Larsen 1996; Ao et al., 2006; Grime and Langley 2008; Iturrizaga et al., 2012; Liu et al., 2014). The relia-bility analysis approach followed in the present study for estimating mean time to failure (MTTF) was based on the field failure data published by the offshore industry. The basic failure-in-time (FIT) data for the mooring subcomponents, as obtained from the relevant data sources, are shown in Table 1. The reliability of the mooring components has improved over the years with improvements in material manufacturing and quality control prac-tices, For example, the reliability of the mooring chains has much improved from a FIT of 9,512 in 1997 (Health and Safety Executive, 1997) to 836 in the present decade (Ma et al., 2013). The base FIT for the swivel and shackles was obtained from the reliability models available in NSWC standards (NSWC, 2011). For the buoyancy floats, thimbles and connecting rings, the experiences from the oil and gas industry, as recorded by the Offshore Reli-ability Data (OREDA) Handbook (OREDA, 2009) was used. The FIT for the PP and nylon ropes was also calculated based on the failures reported by the oil and gas industries from 300 operational moorings during a period from 2001 to 2011 (Ma et al., 2013).

4.2. Computing FIT for the mooring design under extreme operating conditionsBased on the reported NIOT-OOS experiences in maintaining offshore moored buoy networks, a mooring maintenance period of one year was found appropriate for a system operating in a harsh marine environment. Thus, for a maintenance win-dow of one year, reliability analysis was carried out to estimate the probability of failure (PoF) taking into consideration the extreme environmental and physical operating conditions experienced by the mooring subcomponents and the design safety fac-tor for ultimate loading conditions. Tables 2 and 3 show the detailed technical specifications of the carbon steel mooring hardware and ropes used in the mooring for calculating the PoF.

In order to compute the FIT of the mooring components, based on the component reliability models, simulations were performed for the moor-ing system using offshore dynamic analysis. The simulation tools used included the WHOI Cable ( Jason and Mark, 2000) and OrcaFlex (Orcina Ltd, 2015), and were based on the expected environ-mental conditions experienced by the mooring during extreme cyclonic events as inputs. The sim-ulation results, showing the load on the mooring system with the environmental conditions experi-enced during the Phailin cyclone, are shown in Fig 3. During the times where surface wind speeds of around 40ms–1 (India Meteorological Department, 2013) were recorded, the maximum tension esti-mated by the simulations was 0.8tonnes. As a valida-tion for the simulation results, the estimated tension was compared to an actual value recorded by a load cell installed in one of the moorings expe-riencing similar environmental conditions; the recorded peak load was 0.7tonnes. The loads esti-mated in the simulations and the loads recorded in the field were compared with the safe working load (SWL) of the inline mooring components (shown

Table 1: Failure in time for mooring components and data sources

Subcomponent FIT range* Source of data

Shackle 200–450** NSWC (Naval Surface Warfare Center, 2011)Swivel 500Connecting ring 100–500 OREDA (OREDA, 2009)Thimble 100–500Buoyancy floats 1,470–10,500Wire rope 495

Kai-Tung Ma et al. (Ma et al., 2013) Dead weight chain 836Polypropylene rope 114–399 Nylon rope 114–399Anchor chain 700 Fengmei Jing et al. (Jing et al., 2012)

* FIT in billion hours = (number of failures/number of units × operating hours) × 109

** The values indicated in range are with a confidence interval of 90%

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in Tables 2 and 3), and was found to be well within the SWL limits of the mooring components. It was also found that the mooring components design and selection complies well within the DNV recom-mended safety factor as defined by the Accidental Limit State (ALS) (DNV, 2010). The stresses on the mooring components generated during the demand-ing environmental conditions were factored into the mooring components FIT computation models.

4.3. Reliability analysisThe mooring hardware was categorised into five sections and the failure trees were made using the

fault tree analysis module of the TOTAL-SATODEV GRIF tool and the PoF was computed using the upper FIT range values shown in Table 1. Fig 4 shows the failure trees computing the PoF for the mooring over a one-year period based on exponen-tial law.

From the failure tree analysis, the PoF of the mooring over a one-year period (as it was under annual maintenance) was 14.7%, which corre-sponds to an MTBF of 6.3 years and failure rate of 18,100 FIT. Fig 5 shows the PoF of the expanded view of the Nylon rope-PP section with reference to Fig 4. The computations were also made taking into account the lower bound component failure rates and it was found that the PoF of the moor-ing in a period of one year was 8.63%, which cor-responded to an MTBF of 11.1 years and a reliability of 10,300 FIT. Thus, based on global failure data, the MTBF could be in the range from 6.3 to 11.1 years.

5. Achieved mooring reliability and availabilityThe buoy mooring systems were subjected to con-tinuous improvements based on the OOS field fail-ure experiences and the failure mode effect and criticality analysis (FMECA) studies based on MIL HDBK 217F standards (US Department of Defense, 1991). Table 4 shows the year-wise offshore moor-ing hours, reported mooring failures as recorded by the OOS failure reporting, analysis and the cor-rective action system (FRACAS) register. The com-puted FIT and the corresponding MTTF are also shown. The average mooring MTTF in the five-year period, with about 0.76 million in-place moor-ing hours, was calculated as 6.94 years.

Table 2: Specifications of mooring hardware

Hardware Dia (mm) Safe working load (t)

Proof load (t) Minimum breaking load (t)

Bow shackle 25 8.5 17 51Bow shackle 19 4.75 9.5 29Short link chain 25 8.5 17 51Connecting ring 25 8.5 17 51Swivel 25 8.5 17 51Bearing swivel 25 5 10 50

Table 3: Specifications of mooring ropes

Item description Material Diameter (mm)

Weight (kg/100m)

Breaking load (t)

Combination wire rope Wire rope with polypropylene sheathing 18 123 11.5Nylon rope Nylon 16 16 6.5Polypropylene rope Polypropylene 18 18 6.9

0

500

Maximum MinimumMean

1000

1500

2000

1000 800

Morning effective tension (kgf), t = –12.000 to 100.000s

Arc

leng

th (

m)

600 400 200 0

Fig 3: Simulation results from OrcaFlex showing the tension forces acting on the mooring during Phailincyclone conditions

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Mooring failure

CWR-Nylon ropeinterface section

PP rope-Chaininterface section

PP

Chain-Anchor

Chain-Anchorinterface section

Chain-CWRinterface section

Chain-CWR

CWR-Nylon

Nylon-PP ropeinterface section

Nylon

Or7Q(8760.0) = 0.147

Or5Q(8760.0) = 1.6506E-2

Or6Q(8760.0) = 4.8856E-2

Or4Q(8760.0) = 1.9362E-2

Or2Q(8760.0) = 4.511E-2

Or3Q(8760.0) = 2.6245E-2

+

Fig 4: Probability of failure (PoF) of the mooring in a one-year period

Nylon-PP ropeinterface section

Nylon rope failureFloats failure

Evt7exponential ropeQ(8760.0) = 4.6495E-3

Evt8exponential thimbleQ(8760.0) = 1.7505E-3

Evt10exponential shackleQ(8760.0) = 5.6778E-3

Evt9exponential connecting ringQ(8760.0) = 1.7505E-3

KOutOfN12/6Q(8760.0) = 2.4548E-2

Floats

Evt11exponential thimbleQ(8760.0) = 1.7505E-3

Evt12exponential shackleQ(8760.0) = 5.6778E-3

Thimble failure

Thimble failure

8 10 9

3/4” Shackle failure

3/4” Shackle failure Connecting ring

12117

Or2Q(8760.0) = 4.511E-2+

Fig 5: Probability of failure (PoF) of the nylon rope- polypropylene rope interface section

Table 4: Mooring performance data from 2010 to 2014

Year In-place mooring hours Failures reported Computed FIT MTTF (in years)

2010 27,240 3 110,132 1.042011 150,576 5 33,255 3.442012 167,569 3 17,903 6.402013 203,112 2 9,847 11.62014 213,005 2 9,384 12.2

* FIT in billion hours = (number of failures/in-place mooring hours) × 109

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The following case studies produced the return of experiences (RoE) which helped in improve system reliability:

5.1. RoE1: Nylon-wire rope interface failureIn November 2010, a METOCEAN buoy which was installed originally at 18010’01”N–89040’00”E in the Bay of Bengal during October 2010, at a water depth of 2,151m, was found drifting. The hardware was recovered and it was found that the portion of the mooring up to nylon rope had become parted along with the surface buoy. Analysis revealed that the cause for the failure was abrasion of the wire rope against the nylon rope. In order to avoid simi-lar future failures, a connecting ring of safe work-ing load (SWL) 8.5tonnes was introduced to all the moorings. This eliminated direct contact between the wire and nylon ropes. Subsequently this type of failure has not occurred since. Fig 6a shows the failed portion and Fig 6b shows the incorporated modification.

5.2. RoE2: Mooring walkIn August 2011, a METOCEAN buoy installed orig-inally at 09056’38”N–88028’28”E in the Bay of Bengal at a water depth of 3,523m, was found drifting off the deployed location, a day after deployment. It was identified using the buoy watch circle feature, which is defined based on the mooring design con-sidering the mooring scope and the allowable radius for the buoy drift. This feature is developed and implemented by the MCC (Sundar et al., 2013). Even though the buoy was recovered by coming away from the mooring during extreme weather conditions, failure modes and effects analysis (FMEA) studies were done using post analysis by the MCC. Using the buoy position data it was found that the buoy had drifted at an average velocity of 10 cm s–1 (Fig 7). It was identified that the ‘mooring walk’ had occurred because the mooring’s deploy-ment depth was greater than that it was designed

for. Even though standard deployment and retrieval procedures (SDRP) carried out a bathymetry sur-vey at the designated location prior to deployment, undulations in the seabed could have hidden the true depth of the mooring’s deployment. Subse-quently, SDRP was modified to incorporate a more detailed bathymetry survey prior to each future deployment.

5.3. RoE3: Polypropylene rope failureIn December 2011, a METOCEAN buoy was installed originally at 18059’42”N–67000’10”E in the Arabian Sea at a water depth of 3,215m and a tsunami buoy was installed originally at 20038’37”N–67005’35”E in the Arabian Sea at a depth of 3,059m; both were recovered following mooring failures. The METOCEAN buoy was located in high seas after identifying floating PP rope close to the location recorded by the MCC; Fig 8 shows the recovered buoy and the parted nylon rope. Similarly, the tsu-nami buoy was found off the deployed location; the drift, as recorded by the MCC, is shown in Fig 9.

Based on these experiences, it was found that the main cause of the failures included improper use of interfaces, plus quality and manual deploy-ment errors. In order to avoid such failures in the

Fig 6: (a) Mooring failure hardware and (b) improvements incorporated in future systems

Fig 7: Mooring walk data recorded at the Mission Control Centre (MCC)

Fig 8: A meteorological ocean (METOCEAN) buoy retrieved back on vessel

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future, quality control procedures include: insisting on test certificates from the hardware supplier; and prior training for the deployment personnel fol-lowing SDRP. In addition, it was considered likely that standardising mooring design would reduce the potential for future failures.

5.4. RoE4: Corrosion related failureIn May 2013, a coastal buoy installed at 10052’33”N–72012’40”E near Agatti Island, at a depth of 20m, was operational for 104 days following deployment. However, it was subsequently reported drifting and was recovered; the failure was caused by marine corrosion of the stainless steel mooring lug (SS316) resulting in slippage of the interface from the mooring system. As the nut failed the entire buoy became detached and drifted away, leaving the moor-ing in place. The buoy architecture involved the mooring leg being attached to the bottom portion of the buoy hull using long bolted fasteners. A study of the corroded nut, using a 126X scanning electron microscope (SEM), revealed that the failure was caused by a quality defect of the surface-treated fas-tener. Fig 10 shows a comparison of a new nut against the corroded one (Fig 10a), and the SEM image of the corroded nut (Fig 10b). Following this experi-ence, quality was ensured on all fasteners used in

future moorings. Similar experiences with corrosion-related failures have been reported previously (Venkatesan et al., 2014).

Investigations on failed moorings have, there-fore, revealed that failures could be reduced by ensuring that the quality of the materials being used is suitable for operations in the marine environ-ment; that mooring configurations are reliability-centered; and that deployment methodologies are suited to the depth and location of release. In addi-tion to the SDRP being followed, the procedures involved at the various stages of deployment, from initial design to post-installation monitoring, must be standardized. These should be based on the les-sons learned from past experiences, the quality requirements, and the safety needs and best prac-tices used in similar buoy systems (Venkatesan et al., 2012) such as the National Oceanic and Atmos-pheric Administration-Pacific Marine Environmen-tal Laboratory (NOAA-PMEL) and the National Data Buoy Center (NDBC) of the USA. The imple-mentation of these procedures can be divided into the hardware realisation, and then the pre-deployment, deployment and post-deployment phases. The hard-ware realisation phase covers design, procurement and testing methods. The pre-deployment phase cov-ers component verification, packing, safe onboard storage, mooring assembly checks, deployment pre-check by experienced personnel, and checklists. The deployment phase covers having reliable and safe deployment deck-gear, launching in fair weather conditions, the vessel orientation with respect to the mooring, the time taken and the deck-load confir-mation when the anchor lands on the seabed. The post-deployment phase involves verifying that the deployed location is correct and stable by following up on that deployment through the MCC with the aid of data received by the MCC from the buoy.

5.5. AvailabilityAvailability defined below is a function of the mean time to failure (MTTF) and mean time to repair (MTTR).

Availability in % = [MTTF / (MTTF + MTTR)] × 100

MTTF is a function of the intrinsic reliability of the system, and the MTTR depends on the technical and management strategies adopted in the early restoration of the system, during which period it is unavailable for operation. Based on OOS expe-rience, the MTTR tends to be around 7 days. Thus, with an MTTF (which is equal to MTBF) of 6.3 years, the availability of the mooring would be 99.72%. Thus, once the target reliability levels have been achieved, a suitable mooring integrity

Fig 9: Trace of tsunami buoy drifting reported to the Mission Control Centre (MCC)

Fig 10: (a) Mooring failure involving nut corrosion induced failure and (b) the 126X scanning electron microscope image of material defect

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management system would work to reduce the MTTR to ensure maximum availability.

6. Mooring integrity management 6.1. International Electrotechnical Commission (IEC) 61508 standardsIEC 61508 is a standard which generates a frame-work for implementing instrumented safety systems using the principles of the safety life cycle and Safety Integrated Level (SIL) concepts (IEC 61508, 2000; Bukoswki, 2001; Smith and Simpson, 2004; Exida, 2007; Yoshimura and Sato, 2008). Systems need to perform their intended operations on demand. The PoF is the unavailability of a system on demand and is defined by probability of failure on demand (PFD). SIL defines the degree of safety protection required by the process, and conse-quently the safety reliability of the system necessary to achieve the function. SIL has four levels, 1 to 4; the higher, the safer. Table 5 describes the various SIL levels, with their corresponding PFD.

Based on the IEC 61508, the SIL requirements for the system were computed, taking into consid-eration the risk consequence, the alternative safety instrumented function (SIF) in place, the human occupancy and the demand rate for the SIF. This SIL determination methodology has been applied previously in safety and reliability models (Vedachalam et al., 2014a, b).

6.2. Determination of the required SIL for buoy moorings based on IEC and DNV standardsRisk graph matrices may be used to evaluate the SIL requirements for the system which requires computation of avoidance (F), occupancy (P) and demand rate (W) parameters. Based on the availa-bility or unavailability of the alternative SIF in place, the avoidance parameter F takes a value of 0 or 1. Being a single point mooring, F is taken as 1. Based on the human occupancy, the P takes the values of 2, 1 and 0 corresponding to the continuous, occasional and rare human presence, respectively, in the protection zone. Based on the continuous

human presence on the Indian coastline, the value of P is taken as 2. W is computed based on the system demand during cyclone and tsunami events and is shown in Table 6. Based on the historical data on the occurrence of cyclones and tsunamis, the factor is assigned a value of 9.

Table 7 shows the values taken as the input for the risk consequence parameters, which are cata-strophic in all three defined aspects. Having com-puted the values of, the summed value of P, F and W (which is 12 (1 + 2 9)) is then compared against the consequence factor in the risk graph matrix shown in Table 8. In order to meet the required SIL 4 level of safety reliability with an annual PoF between 10–4 and 10–5 (shown in Table 5), the required proof test interval (PTI) was calculated using the TOTAL SATODEV GRIF SIL module for the achieved reliability/FIT. From Fig 11 it can be seen that the monitoring system should have a PTI of 5.5 hours so as to comply with IEC 61508-SIL 4, with an assumed annual PoF of less than 1 × 10–4.

6.3. Implementation of MIMCASEmerging trends and practices in the offshore indus-try involve mooring integrity management based on

Table 6: Factors for the safety instrumented function (SIF) demand rate (W)

Demand rate Factor (W)

W9 Often (> 1 per year) 9W8 Frequent (1 per 1–3 years) 8W7 Likely (1 per 3–10 years) 7W6 Probable (1 per 10–30 years) 6W5 Occasional (1 per 30–100 years) 5W4 Remote (1 per 100–300 years) 4W3 Improbable (1 per 300–1,000 years) 3

Table 7: Risk level assignment data

Personnel health

Environment Financial

Consequence* Catastrophic Catastrophic Catastrophic

* Catastrophic, extensive, serious

Table 8: Risk graph matrix

Consequence F + P + W

Severity level

C 1,2 3,4 5,6 7,8 9,10 11,12

Catastrophic F NR SIL1 SIL2 IL3 SIL4 > SIL4Extensive E NR NR SIL1 SIL2 SIL3 SIL4Serious D NR NR NR SIL1 SIL2 SIL3Considerable C NR NR NR NR SIL1 SIL2Marginal B NR NR NR NR NR SIL1Negligible A NR NR NR NR NR NR

NR – No protective system required; SIL – Safety Integrity level

Table 5: Probability of failure on demand (PFD) and safety integrity level (SIL) levels per IEC 61508 standards

PFD =Tolerable frequency of the accident

Frequency of the accident wiith no protection

Safety Integrated Level (SIL)

Probability of failure on demand (PFD) per year

1 10–1 to 10–2

2 10–2 to 10–3

3 10–3 to 10–4

4 10–4 to 10–5

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mooring tension monitoring (API, 2008; Petruska et al., 2008; Elman et al., 2013; Gauthier and Elleteson, 2014). The MIMCAS is implemented by monitoring the mooring location through the MCC. The operat-ing buoy systems currently involve data transmission at defined time intervals to the MCC, set at one hour for the tsunami buoy family and three hours for the

other buoys. MIMCAS is implemented through onboard global positioning system (GPS) data being transmitted to the MCC along with the meteorologi-cal data. Fig 12 shows the view of the MIMCAS watch circle feature automated in the MCC to generate an alert if the buoys start to. A PTI of three hours ensures compliance with the IEC 61508 standards

Fig 11: Identified proof test interval (PTI) for the mooring monitoring system to comply with safety integrity level 4 (SIL4)

1.3E-4

1.2E-4

1.1E-4

1E-4

9E-5

8E-5

7E-5

6E-5

5E-5

4E-5

3E-5

2E-5

1E-5

0

0 5E-1 1 1.5 2 2.5 3 3.5

Proof test inverval (PTI; hours)

PFD for the buoy MIMCAS

PF

D

4

SIL3

SIL4

4.5 5 5.5 6 6.5 7

Aerial view of mooring’s elasticity limit(red area)

Watch CircleAerial view of mooring length (green area)

Buoydeployment

location

Latest positiontransmittedfrom buoy

BPRdeployment

location

BPR beamwidth (blue area)

Last 7transmitted

position(yellow)

from buoy

Previoustransmissions

from buoy

Fig 12: A Mission Control Centre (MCC) report of the implemented mooring integrity manage-ment and corrective action system (MIMCAS) (Venkatesan et al., 2015b)

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Table 9: Target reliability to achieve Det Norske Veritas (DNV) targets

PFD Reliability in FIT MTBF

5 × 10–5 18,100 6.3 years4.5 × 10–5 15,000 7.6 years1.5 × 10–5 5,000 22.8 years1 × 10–5 3,450 33 years2.2 × 10–6 750 152 years

and World Meteorological Organization (WMO) requirements (Harper et al., 2010).

7. Future targets With the objective of improved mooring reliability and increased availability, NIOT-OOS aims to achieve the reliability targets set by DNV for offshore position moorings of an annual PoF of less than 1 × 10–5. Table 9 shows the computed reliability (computed using GRIF tool) and MTBF for the DNV-defined target for a PTI of three hours.

Even though the reporting interval could be reduced to achieve the target PFD, increases in the reporting frequency would result in increased costs of data transmission and increased space requirements for onboard energy storage (Venkatesan et al., 2015a). As shown in Table 9, a DNV target with an annual PoF of less than 1 × 10–5 could be met with a mooring reli-ability of 3,450 FIT which corresponds to approxi-mately one failure per year out of 24 buoys in operation. With the improving material technologies, computational capabilities and advancing deploy-ment systems, continuous efforts are being under-taken to attain the stringent DNV recommendations.

AcknowledgementsThe authors thank the Ministry of Earth Sciences, government of India and Indian National Centre for Ocean Information Services (INCOIS) Indian Early Tsunami Warning System (IETWS) for funding this project. The authors are indebted to the Directors of the National Centre for Antarctic and Ocean Research (NCAOR), Goa for providing all the facili-ties and logistic support. The authors also thank the staff of Ocean Observation System (OOS) group, Vessel Management Cell of the NIOT and ship staff for their excellent help and support onboard.

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