KEANDALAN SISTEM A
Nama : Teofilus Dimas Prasetyo
NRP : 04211840000073
RESUME
Judul
: Assessment Of The Reliability of The Indian Tsunami Buoy System
Peneliti
: R Venkatesan, N Vedachalam*, R Sundar, M Arul Muthiah, P Prasad
and MA Atmanand National Institute Of Ocean Technology, Chennai, India
Abstrak
:
Makalah ini menganalisis keandalan Indian Tsunami Buoy System(ITBS) dari sudut pandang
kepentingan masyarakat. ITBS dikembangkan dan dipelihara oleh National Institute of
Ocean Technology(NIOT) di Teluk Benggala dan Laut Arab. Sistem ini melibatkan
pengukuran tekanan dasar laut dengan presisi tinggi, dalam komputasi situ dan perangkat
telemetering akustik yang dipasang di dasar laut, yang mengirimkan perubahan anomali di
kolom air ke pelampung permukaan yang ditambatkan.
Latar belakang
:
Peristiwa tsunami 2004 di Asia Selatan (Wijetunge, 2009) mengakibatkan kerusakan besar
pada kehidupan dan harta benda, dan karenanya, terjadi pertumbuhan pesat di minat dalam
deteksi tsunami dan sistem peringatan. Karena tidak ada peringatan yang diterima sebelum
peristiwa itu, lebih dari 240.000 nyawa hilang dari 11 negara di seberang Samudra Hindia
(Groen et al., 2010).
Bencana ini memotivasi Intergovernmental Oceanographic Commission(IOC) dari organisasi
pendidikan, ilmiah dan budaya PBB untuk mengadakan dan membentuk Indian Early
Tsunami Warning System (IETWS), seperti yang dibayangkan oleh IOC pada tahun 2025,
tiga perempat populasi dunia akan tinggal di daerah pesisir (Groen et al., 2010) dengan kotakota besar yang terletak di pesisir.
Hasil
:
Analisa Keandalan
Analisis keandalan menggunakan metode fault three method, dilakukan untuk ITBS, dengan
mempertimbangkan sistem jatuh tempo sejak didirikan pada tahun 2007. Kasus dasar
melibatkan sistem yang dikembangkan dan dipasang di tahun 2007. Selama tahun-tahun
berikutnya, berdasarkan pada efek mode kegagalan dan analisis kekritisan, dan kembalinya
pengalaman teknis dan operasional dari lapangan, sistem itu terus menerus ditingkatkan
dengan fokus utama pada keandalan. Dengan perbaikan yang digabungkan, sistem – yang
beroperasi pada tahun 2014 - dianggap sebagai kasus yang matang. PoF dihitung selama satu
tahun periode untuk kasus dasar dan dewasa. Analisis dilakukan, dengan sistem yang
dikategorikan ke dalam subsistem termasuk BPR, MSB dan LES yang mengaktifkan NIOTMCC.
Analisis keandalan yang dilakukan pada kasus dasar dan sistem yang matang menunjukkan
bahwa MTBF dari simpul lepas pantai tunggal di Teluk Benggala yang berkomunikasi
dengan NIOT-MCC telah meningkat dari 0,3 tahun pada tahun 2007 menjadi 0,9 tahun pada
tahun 2013. Teridentifikasi bahwa, dengan ITBS yang sudah beroperasi dan matang, adalah
mungkin untuk memiliki MTBF lebih dari 1,6 tahun dan 1,4 tahun dengan empat dan satu
simpul lepas pantai masing-masing di Teluk Bengal dan Laut Arab. Namun, berdasarkan
kebutuhan pengisian ulang baterai dan kebutuhan perawatan sensor, jadwal pemeliharaan
ditetapkan sebagai satu tahun, yang dapat menyediakan ketersediaan lebih dari 98,5%.
Berdasarkan analisis HSE berbasis IEC, diidentifikasi bahwa sistem ITBS harus memenuhi
tingkat keandalan SIL 4
Komentar
:
Pada analisa keandalan indian tsunami buoy system ini hanya menggunakan satu metode
analisa, yaitu metode fault three method. Menurut saya apabila peneliti tidak hanya
menggunakan satu metode analisa tetapi beberapa metode analisa keandalan mungkin hasil
dari analisa yang telah dilakukan akan akan lebih baik dan akurat lagi.
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/280736387
Assessment of the reliability of the Indian tsunami buoy system
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Assessment of the reliability of the Indian
tsunami buoy system
R Venkatesan, N Vedachalam*, R Sundar, M Arul Muthiah, P Prasad and MA Atmanand
National Institute Of Ocean Technology, Chennai, India
Technical Paper
doi:10.3723/ut.32.255 Underwater Technology, Vol. 32, No. 4, pp. 255–270, 2015
Received 29 July 2014; Accepted 24 October 2014
Abstract
The present paper analyses the reliability of the Indian tsunami
buoy system (ITBS) from the point of view of societal importance. The ITBS was developed and maintained by the
National Institute of Ocean Technology (NIOT) in the Bay of
Bengal and Arabian Sea. The system involves high-precision
ocean bottom pressure measurement, in situ computation
and acoustic telemetering devices installed on the ocean floor,
which transmit the anomalous change in the water column to
the moored surface buoy. The surface buoy, in turn, relays
data to the mission control centre shore station for predicting
and advancing decisions in the event of a tsunami. The system has to be operational throughout the year and alert the
Indian coastline in the event of a tsunami, and therefore the
system should be trustworthy. Since its inception in 2007, the
system has undergone many technological improvements
with the main focus being on reliability. The health, safety and
environment analysis indicates that, with one offshore node
reporting its health every 10 hours to the mission control centre, the system could comply with the stringent Safety Integrity
Level (SIL) 4. The result is found to comply with the actual
system where reporting every hour is being implemented. The
present study gives confidence on the ITBS’s reliable support
to the Indian Early Tsunami Warning System.
Keywords: buoy, health safety environment, mean time
between failure, reliability, safety integrity level, tsunami
IETWS
INCOIS
IOC
ITBS
LES
MCC
MSB
MTBF
MTTR
NCAOR
NIOT
NIOT-MCC
NOAA
OMNI
OOS
PFD
PMEL
PoF
PTI
SIL
TM
UPS
Indian Early Tsunami Warning System
Indian National Centre for Ocean Information
Services
Intergovernmental Oceanographic
Commission
Indian tsunami buoy system
land earth station
Mission Control Centre
moored surface buoy
mean time between failure
mean time to repair
National Centre for Antarctic and Ocean
Research
National Institute of Ocean Technology
National Institute of Ocean Technology,
Mission Control Centre
National Oceanic and Atmospheric
Administration
Ocean Moored buoy Network for Northern
Indian Ocean
Ocean Observation Systems
probability of failure on demand
Pacific Meteorological Environmental
Laboratory
probability of failure
proof test interval
Safety Integrity Level
technical maturation
uninterrupted power supply
Acronym list
ADDRESS
BPR
DART
FIT
FRACAS
HSE
advanced data reception and analysis system
bottom pressure recorder
Deep Ocean Assessment and Reporting of
Tsunamis
failure-in-time
Failure Reporting, Analysis, and Corrective
Action System
Health, Safety and Environment
* Contact author. Email address: veda1973@gmail.com
1. Introduction
The 2004 tsunami event in South Asia (Wijetunge,
2009) resulted in vast destruction to life and property, and hence, there has been a rapid growth in the
interest in tsunami detection and warning systems.
As no warning was received prior to the devastating
event, more than 240,000 lives were lost from 11
nations across the Indian Ocean (Groen et al., 2010).
255
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
This disaster motivated the Intergovernmental
Oceanographic Commission (IOC) of the UN educational, scientific and cultural organisation to convene and establish the Indian Early Tsunami Warning System (IETWS), as the IOC envisages that by
the year 2025, three-quarters of the world’s population will be living in coastal areas (Groen et al.,
2010) with major cities located within the coastal
line.
The tsunami forecast by the IETWS involves the
combined analysis of seismic signatures, water level
changes in the deep ocean and near-shore tidal
observations (Venkatesan et al., 2013). Real-time
data are specifically for detecting and reporting the
water column changes in deep sea locations, so as
to allow the forecasting centres to generate lifesaving early warnings of tsunamis (Fine et al., 2005;
Brown, 1983; Gasiewski et al., 2005). The Ministry of
Earth Sciences, Government of India, has taken up
the project on the development and deployment of
the Indian tsunami buoy system (ITBS) in Indian
waters. The real-time data are received at the
National Institute of Ocean Technology, Mission
Control Centre (NIOT-MCC) in Chennai, and the
Tsunami Warning Centre located in the Indian
National Centre for Ocean Information Services
(INCOIS) in Hyderabad, India, for data modelling
and public notification in the event of a tsunami
(Arul Muthiah et al., 2010; Srinivasa Kumar et al.,
2012). The decision support system at INCOIS is
supported by real-time modelling software, which
receives data from the described ITBS, real-time
seismic monitoring network with 17 nodes and tide
gauge network at 36 locations (INCOIS, 2011).
The IETWS is vital for the well-being of the coastal
population, where the accuracy of data and its timely
receipt and proper interpretation can have life-ordeath consequences. In addition, false alarms lead
to huge unrest, unwarranted evacuation exercises
and expenses. Moreover too many false alarms would
weaken the faith in the system. Thus, the reliability
of the ITBS is a key factor, as the failure of the system to detect and report a likely event cannot be
tolerated. Therefore, the failure rate of the system
has to be as low as reasonably possible and with the
highest level of availability.
Reliability modelling is done for the operating
ITBS, with the failure data based on the National
Institute of Ocean Technology (NOIT) experiences
and relevant standards so as to identify the mean
time between failure (MTBF) and identify the minimum required offshore nodes to ensure the maximum system availability. In order to ensure that the
probability of failure of the system on-demand is
minimal and meets the required Safety Integrity
Levels (SILs), the frequency of the proof test inter256
vals are analysed, based on the IEC 61508 Health,
Safety and Environment (HSE) demands. This
analysis helps in identifying and improving the subsystems which have significant failure probabilities.
2. Importance of ocean observation
programme for India
A tsunami is an ever-present threat to lives in the
7,500km-long Indian coastline, where 30% of the
national population resides. In the Indian Ocean,
there are two tsunamigenic zones: the AndamanSumatra trench and the Makran coast. The 2004
Indian Ocean tsunami was one of the most ruining
disasters in modern history, and the second largest
earthquake ever recorded on a seismograph (Arul
Muthiah et al., 2010; Wijetunge, 2009). The Ocean
Observation Systems (OOS) programme, the former
National Data Buoy Program of NIOT, was established in 1996 with the prime objective to operate,
maintain and develop moored buoy observational
networks and related telecommunication networks
in the Indian waters. The OOS has inherited the
lead responsibility for a number of important and
well-established observational programmes.
The NIOT-MCC manned centre receives data
non-stop from moored buoys located in the Indian
Ocean (Kesava Kumar et al., 2013). The Indian
moored buoy system comprises three families:
• Meteorological ocean buoys for measuring and
telemetering the meteorological and sea surface
parameters;
• Ocean Moored buoy Network for Northern Indian
Ocean (OMNI) for measuring and telemetering
the meteorological, surface and subsurface parameters; and
• ITBS for the detection and reporting of the
water level for tsunami warning.
As part of the IETWS established by the Indian
government at the INCOIS in Hyderabad in 2007,
tsunami buoy networks to detect tsunamis were
installed and operated by NIOT, and the same is
shown in Fig 1. In addition, the INCOIS maintains
two tsunami buoys supplied by the SAIC in the US,
which is not discussed here.
The main objective is to collect and disseminate
water column changes, using bottom pressure recorders (BPR) deployed at selected locations in the Bay
of Bengal and Arabian Sea. Based on the mandate
of the government, NIOT uses Inmarsat satellite
telemetry systems since 1997, for real-time data
transmission and two-way communications in all
moored buoy systems. Since the first meteorological ocean buoy was deployed off Chennai, nearly
550 moored buoy systems have been deployed
Underwater Technology Vol. 32, No. 4, 2015
Fig 1: ITBS network in the Indian Ocean
for collecting various meteorological, surface and
subsurface parameters, and tsunami water level
data, ranging from coastal waters to deep oceans.
The range of coverage of the deployment of these
buoys is 63°E to 93°E and 6°N to 20°N. The buoy
systems were used to record extreme weather conditions, such as 17 cyclones from 1997 to 2012, collecting nearly 4.4 million datasets over 15 years.
From 1997 onwards, the NIOT-MCC has aggregated more than 2.5GB of ocean datasets. The ITBS
data are being well accepted by IOC on a reliable
basis and exchanged with other countries for ensuring coordinated effort during a tsunami event and
for future analysis. On 12 June 2010, when an
undersea earthquake occurred, the ITBS deployed
TB04 in the Bay of Bengal and captured the seismic
signal, which originated from 7.848° N to 91.919°
E. It then switched to the tsunami mode and transmitted the captured data to the shore centres, and
the data were analysed (Arul Muthiah et al., 2010)
for a possible tsunami. The operational tsunami
monitoring nodes in the Indian waters are shown
in Table 1.
3. Indian tsunami buoy system for early
tsunami warning
The concept involved in the tsunami detection and
reporting system developed by the NIOT shown
in Fig 2, is similar to the robust and proven Deep
Ocean Assessment and Reporting of Tsunamis
(DART) developed by the US National Oceanic
and Atmospheric Administration (NOAA), Pacific
Meteorological Environmental Laboratory (PMEL)
(Meinig et al., 2005), and is operational in the Pacific,
Atlantic and Caribbean waters. A tsunami wave in
deep water creates a small but measurable change
in pressure that will persist for a period of 20mins
(Ramadass et al., 2014). By monitoring and analysing any such changes, subsea detectors can be used
to trigger an alarm that sends a warning message to
a moored buoy-mounted receiver on the sea surface. The buoy, in turn, relays the message via a satellite datalink to a control centre that can issue a
warning to vulnerable communities.
An offshore node involves a system using a BPR,
deployed in the locations indicated in Table 1 in deep
Table 1: Tsunami monitoring offshore nodes in the Indian Ocean
Buoy ID
World Meteorological
Organization ID
Latitude (N)
Longitude (E)
Depth (m)
ITB03
ITB05
ITB06
ITB09
ITB12
23217
23219
23220
23223
23226
06° 20’54”
10° 59’ 49”
14° 44’06”
17° 28’ 57”
20° 20’ 47”
88° 35’ 36”
89° 28’ 55”
89° 33’ 48”
89° 46’ 57”
67° 32’ 11”
3,966
3,220
2,725
2,284
3,050
257
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
SATELLITE
SHORE STATION
WATER LEVEL
SURFACE BUOY
SUBSURFACE FLOAT
ACOUSTIC LINE
BPR SENSOR
MOORING
4,000m (app)
SURFACE ACOUSTIC MODEM
SEABED
Fig 2: Concept of ITBS developed by NIOT
waters, and the locations are based on the fault line
and the shoreline to be protected (Ramadass et al.,
2014). The BPR is accompanied by an integral pressure averaging subsystem and a deep ocean pressure
recorder unit (Sundar et al., 2013). The pressure
sensor in the BPR uses a very thin quartz crystal beam,
electrically induced to vibrate at its lowest resonant
mode and having a measurement sensitivity better
than 2 × 10–7, which can measure a water column
change of <1mm in a 6,000m water column (Srinivasa
Kumar et al., 2012; Yilmaz et al., 2004). A co-located
electronic processor acquires the signals from the
pressure sensor continuously, and the in-built algorithm continuously analyses the water column pressure changes and indicates the possibilities of a
tsunami event (Arul Muthiah et al., 2010). The power
required for the subsea data processing, using an
electronic data processing system and acoustic
modem, is supplied by the BPR located primary
lithium thionyl batteries of 2000Ah capacity, which
are designed to work for 1.5 years (Kesava Kumar
et al., 2013) in normal operating conditions.
The subsea equipment and the buoyancy package are mounted on a frame that is moored with
an anchor weight and an acoustic release, making
it possible to retrieve by sending a release command to the acoustic modem. The software in the
BPR processes the acquired pressure data based on
the predictive Newton Forward Extrapolation
algorithm, developed by the NOAA (Meinig et al.,
2005; Ramadass et al., 2014). The BPR telemeters
the data acoustically with time-stamped pressure
readings and system status parameters. The transceiver in the moored surface buoy (MSB) receives
the acoustically transmitted data from the BPR.
258
The MSB transceiver is capable of two-way communication to request status information from the BPR
and to change the settings in the same. The features
include BPR wake-up, change detection parameters, forcing and cancelling an event, request battery status, starting and stopping of data logging,
and the mechanism to trigger the acoustic release
of the BPR.
The MSB transmits the data to the Inmarsat satellite terminal, which, in turn, transmits it to the
NIOT-MCC and INCOIS data analysis centre, through
the land earth station (LES) and public network
systems. The application software operating in the
Mission Control Centre (MCC) decodes and disseminates the data for display (Sundar et al., 2013).
The daily power requirement of the MSB is about
8AH met with secondary and primary batteries with
a peak current of 2.6A, which is consumed by the
satellite transceiver for a few seconds during data
transmission. The 100AH capacity lead-acid battery
in the MSB is charged using a set of four solar panels, each of 12V, 20W capacities.
Considering the overall cost involved in the maintenance of buoys located in remote sites far away
from the shore, the secondary batteries are backed
by 3,000AH capacity lithium thionyl chloride primary
batteries. The Li batteries are characterised by low
self-discharge, high energy density (FIDES, 2010),
wide operating temperatures, high pulse current handling capacity and a stable voltage profile throughout the discharge period (Venkatesan et al., 2013;
Meldrum, 2009), which are significant advantages
for the MSB requirements. The 24hr manned NIOTMCC is powered by the electricity mains, backed by
two 125kVA generator sets in the automatic mains
failure mode, and further backed by three redundant uninterrupted power supply systems. The probability of failure (PoF) of power to the MCC without
and with uninterrupted power supply (UPS) is computed to be 20% and 1%, respectively, in a one-year
period. Fig 3 shows the dashboard of the advanced
data reception and analysis system (ADDRESS) displayed in the MCC.
As shown in Fig 1 earlier, tsunami buoys are placed
at strategic locations in the Arabian Sea and Bay of
Bengal to provide early warnings. Strategic sections
of locations close to the Andaman-Sumatra subduction fault in the Bay of Bengal and Makran fault in
the Arabian Sea for deployment of tsunami buoys are
determined by the careful consideration of many
factors (Srinivasa Kumar et al., 2012). Importantly,
the tsunami buoy has to be installed far away from
any potential earthquake epicentre, to ensure that
there is no interference between the earthquake
signal at the buoy and the sea-level signal from the
tsunami. On the other hand, the tsunami buoy needs
Underwater Technology Vol. 32, No. 4, 2015
Fig 3: View of the ADDRESS screen in NIOT-MCC (reproduced with permission from NIOT)
to be close enough to the epicentre to enable timely
detection of any tsunami, so as to maximise the
lead time of tsunami forecasts for the coastal areas.
Seven locations, i.e. five in the Bay of Bengal and
two in the Arabian Sea, are selected by India, considering all these factors and the international maritime boundaries. Fig 4 shows one of the offshore
nodes in operation.
For an effective IETWS, time is of essence. Braddock and Carmody (2001) specify the time constraint for the system as:
T1 + T2 + T3 ≤ T4
(1)
where T1 is the detection time, T2 is the assessment
time, T3 is the evacuation time, and T4 is the tsunami travel time. Detection time can be reduced by
optimising the locations of seismic stations, tsunami
nodes and tide gauges, by implementing global
real-time data telemetry for the monitoring system
(Holgate, 2007), and by improving data processing
and model algorithms.
Shortening the assessment time requires improvements in the ability to measure the true size and
seismic energy of very large earthquakes so as to
understand factors contributing to tsunami generation and to make warning decisions using limited
historical data samples. The evacuation time T3 of
a community is affected by its emergency planning,
education, communication network, and other
socio-economic, environmental and circumstantial
factors. The ITBS detecting and reporting system is
designed in such a way that T1 will be as low as reasonably possible. The reliability analysis of the ITBS
is done with a four node configuration in the Bay of
Bengal and one node in the Arabian Sea.
4. Reliability estimation methodology
for the Indian tsunami buoy system
The following are the major standards adopted for
computing the failure-in-time (FIT) data for the
system components:
Fig 4: View of the offshore node in operation
• FIDES Guide for the estimation of reliability
for electronic components and systems considering mission and environment specific analysis
(FIDES, 2010);
• MIL HDBK 217F, Military handbook for reliability estimation of electronics equipment (US
Department of Defense, 1991);
259
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
• OREDA Handbook for Offshore Reliability Data
(SINTEF, 2009);
• IEEE 493 IEEE Recommended practice for the
design of reliable industrial and commercial
power systems (Institute of Electrical and Electronics Engineers, 1997).
The sources of data for computing the FIT data for
the system components include:
• NIOT OOS Failure Reporting, Analysis, and
Corrective Action System (FRACAS) database;
• The manufacturer’s data and interpretation suitable for the mission profile;
• Schematics for systems and circuit boards, where
available, using component failure data from the
respective standards, failure rates taking the mission profile, operating conditions and stresses
into consideration.
The fault tree analysis and SIL modules of TOTALSATODEV GRIF tool are used for computing the
probability of failure and SILs.
4.1. Reliability estimation
The reliability analysis, using the fault tree method,
is done for the ITBS, taking into consideration
the system maturity since its inception in 2007. The
base case involves the system developed and installed
in the year 2007. Over the subsequent years, based
on the failure mode effect and criticality analysis,
and the returns of technical and operational experiences from the field, the system was continuously
improved with the main focus on reliability. With
the improvements incorporated, the system – which
was operational in the year 2014 – is considered as
a mature case. PoF is computed for a one-year
period for both the base and mature cases. An analysis is performed, with systems being categorised
into subsystems including BPR, MSB and LES activated NIOT-MCC.
4.2. Failure-in-time data for subsystems
and components
Table 2 details the calculated FIT data of the system
and components in failures per billion hours, and
Table 2: Tsunami buoy location data
Subsystem/component
Bottom pressure recorder
Communication circuit board
Communication software
Acoustic receiver
Internal electronics
Data acquisition board
Pressure sensor
Buoyancy/flotation material
Moored surface buoy
Mast/sensor arm installed in Bay of Bengal
Mooring
Acoustic modem
Lead acid battery, 12V, 100AH
Data acquisition board hardware
Data acquisition board software
Power regulation board
Data processor board
NIOT-Mission Control Centre
Electricity mains failure
Generator sets
Automatic mains failure panel
Low voltage circuit breaker
Power transformer
Primary data link between LES and NIOT-MCC
Secondary data link between LES and NIOT-MCC
Server processor
Server software
40kVA UPS
20kVA UPS
OTHERS – General
DC-DC converter
Fuse
Communication cables and connectors
Power cables and connectors
FIT*
Source of data
2000
300
20
2700
500
60
1920
COTS-FIDES
COTS-FIDES
OREDA
COTS-FIDES
COTS-FIDES
OREDA
OREDA
28500
22800
147
3140
3000
28500
86
59
FRACAS-NIOT
FRACAS-NIOT
OREDA
US Dept. of Energy
COTS-FIDES
FRACAS-NIOT
COTS-FIDES
COTS-FIDES
4166667
12000
300
300
673
1260000
220000
0.6
22800
114000
114000
FRACAS-NIOT
FRACAS-NIOT
FRACAS-NIOT
IEEE
IEEE
FRACAS-NIOT
FRACAS-NIOT
FRACAS-NIOT
FRACAS-NIOT
FRACAS-NIOT
FRACAS-NIOT
7
10
25
100
COTS-FIDES
COTS-FIDES
OREDA
OREDA
* FIT in billion hours = (number of failures/number of units × operating hours) × 109
260
Underwater Technology Vol. 32, No. 4, 2015
The MTBF of the base case ITBS is 0.31 years and
it requires frequent maintenance, which is costly in
terms of the ship time and other logistics involved.
Based on the FRACAS data and FMECA studies,
the system is matured with technical improvements
over the years with reliability as the key driver. The
technical maturations (TM) and their effect on the
ITBS MTBF are detailed in the following sections.
is used for the computations in the base case. The
FIT for the components, based on OOS field return
of experiences, is computed using the number of
failure events recorded in the OOS-maintained
FRACAS database, and the number of units in operation during the considered period.
4.3. Base case
The PoF for the base case system involving a single
node in operation in the Bay of Bengal, computed
for a one-year period, is shown in Fig 5. Based on
the physical and functional topologies, failure trees
are determined, with the systems categorised in
BPR, MSB and MCC locations. It can be seen that
the failure contributions of BPR, MSB and MCC are
5.2%, 63.8% and 88.2%, respectively, and results in
the PoF of 95.95%, which corresponds to an MTBF
of 0.31 years. The contributions of the subsystems
are shown in Figs 6 to 8 in chronological order.
From Fig 6, it can be identified that the failure
contribution of the solar panel and the mast
antenna are significant in MSB, each contributing
to 22.12% of the overall data transmission failure of
39.61%. Fig 7 shows the portion of the failure tree
for the communication failure between the MSB and
the satellite terminal, where the contribution of the
firmware failure is 22.09% in the overall communication failure of 24.49%. Fig 8 shows the portion of
the failure trees for the tsunami data receipt failure
at NIOT-MCC, where the contributions of the primary and secondary datalink failure between LES
and NIOT were found to be significant with 85.5%
failure probability.
4.3.1. TM1: Replacement of reliable energy
storage in moored surface buoy
Based on the fault tree analysis shown in Fig 6 and
the NIOT FRACAS data, the failure of the solar
panel charging system and the mast antenna were
mainly caused by the act of external elements prevailing in the deployed location. Damage to the
solar panels lead to charging failure of the lead acid
battery, and damage to the Inmarsat mast antenna
results in the loss of communication between the
offshore node and the MCC. A solution to the
operational challenge is found by incorporating a
reliable lithium thionyl chloride based primary battery pack (Linden and Reddy, 2002) of 2,176AH
capacity in the MSB. The battery pack comprises
eight parallel modules, each of 272AH capacities.
With the buoy consumption of around 5AH per day,
the battery pack can cater to the energy requirements of the MSB for a one-year period. The status
of the battery packs is intimated to the MCC for
management decisions.
Based on the NIOT experiences, the mean time
to repair (MTTR) will be a maximum of seven days,
which involves the ship mobilisation time, travel to
Tsunami warning
system failure
Or25
U(8760h)=0.9595
mission-time=8.76E3
Mission Control
Centre (MCC) failure
BPR system failure
Surface buoy system
failure
Or24
U(8760h)=0.882
mission-time=8.76E3
Or15
U(8760h)=5.254E-2
mission-time=8.76E3
Or22
U(8760h)=0.6383
mission-time=8.76E3
MCC server failure
SBS communication
(DAS) failure
Data transmssion to
IMMARSAT failure
Or18
U(8760h)=0.2449
mission-time=8.76E3
Or21
U(8760h)=0.3961
mission-time=8.76E3
Or1
U(8760h)=6.8008E-3
mission-time=8.76E3
SBS commn
Data
Control and
communication of
BPR failure
Positioning of surface
buoy failure
Or20
U(8760h)=0.2084
mission-time=8.76E3
Positioning
Data failure from
acoustic receiver
MCC server power
input failure
BPR processor failure
to acquire data
MCC server
MCC power
Data failure from
NIOT server station
BPR mechanical
hardware failure
Or16
U(8760h)=2.4315E-2
mission-time=8.76E3
Control
Or17
U(8760h)=8.7562E-4
mission-time=8.76E3
Or10
U(8760h)=4.8471E-3
mission-time=8.76E3
BPR processor
Or14
U(8760h)=2.287E-2
mission-time=8.76E3
Or23
U(8760h)=0.1813
mission-time=8.76E3
Or38
U(8760h)=0.8552
mission-time=8.76E3
Data from server
BPR
Data from acoustic receiver
Fig 5: Tsunami warning failure for ITBS in Bay of Bengal
261
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
Data transmssion to
Inmarsat failure
Or21
U(8760h)=0.3961
mission-time=8.76E3
DAS to modem cable
failure
Antenna mast/sensor
arm failure
Power failure
Or35
U(8760h)=0.2234
mission-time=8.76E3
49
47
Evt49
exponential uwcommncables
U(8760h)=2.1898E-4
mission-time=8.76E3
Evt47
exponential mast
U(8760h)=0.2212
mission-time=8.76E3
Battery failure
Solar panel failure
And34
U(8760h)=2.8651E-3
mission-time=8.76E3
Satellite telemetry
modem failure
74
48
Evt74
exponential mast
U(8760h)=0.2212
mission-time=8.76E3
Battery pack – 1
failure
Or32
U(8760h)=5.3527E-2
mission-time=8.76E3
Evt48
exponential Sat Terminal
U(8760h)=1.2869E-3
mission-time=8.76E3
Battery pack – 2
failure
Or33
U(8760h)=5.3527E-2
mission-time=8.76E3
Battery
Battery pod failure
Battery pod failure
71
70
Evt71
exponential SBSbatterypod
U(8760h)=2.7132E-2
mission-time=8.76E3
Evt70
exponential SBSbatterypod
U(8760h)=2.7132E-2
mission-time=8.76E3
Fig 6: Data transmission to Inmarsat failure from the moored surface buoy
SBS communication
(DAS) failure
Or18
U(8760h)=0.2449
mission-time=8.76E3
Surface buoy power
board failure
Surface buoy data
acquisition board failure
37
35
Evt37
exponential SBSpowerboard
U(8760h)=7.5308E-4
mission-time=8.76E3
Evt35
exponential SBSdataboard
U(8760h)=2.5938E-2
mission-time=8.76E3
Battery failure
And34
U(8760h)=2.8651E-3
mission-time=8.76E3
data
Surface buoy data
processing board failure
Interface cable from
battery to DAS failure
MSB software failure
36
69
75
Evt36
exponential SBSprocessorboard
U(8760h)=5.1671E-4
mission-time=8.76E3
Evt69
exponential uwpowercables
U(8760h)=8.7562E-4
mission-time=8.76E3
Evt75
exponential SBSsw
U(8760h)=0.2209
mission-time=8.76E3
Fig 7: Data acquisition system failure in moored surface buoy
262
Underwater Technology Vol. 32, No. 4, 2015
Data failure from NIOT
server station
Or38
U(8760h)=0.8552
mission-time=8.76E3
NIOT mail server
functional failure
Power failure to NIOT
mail server
And36
U(8760h)=0.8544
mission-time=8.76E3
Or37
U(8760h)=5.351E-3
mission-time=8.76E3
Primary fibre data link
to server failure
Secondary fibre link
to server failure
Lacalised MCB at mail
server room
76
77
78
Evt76
exponential Pylink
U(8760h)=1
mission-time=8.76E3
Evt77
exponential SyLink
U(8760h)=0.8544
mission-time=8.76E3
Evt78
exponential LVCircuitbreaker
U(8760h)=2.6245E-3
mission-time=8.76E3
Power dispatch from
EB section failure
Or6
U(8760h)=2.7336E-3
mission-time=8.76E3
MCC power
Fig 8: Data reception failure at NIOT-MCC
the site, recovery, repair and reinstallation. During
this period, the energy requirement could be around
35AH. When the fault is reported on the 358th day
after deployment, 386AH of energy (approximately
17%) could reside in the battery bank, in which
each module has 48AH. This energy could be met
with one module in the battery pack. Fig 9 shows
the PoF when the Li battery bank is incorporated
along with the lead acid powered system. The gate
N40 in Fig 9 indicates that the failure of the eighth
module in the Li battery pack results in a complete
power failure in the MSB. By means of this modification, the PoF of operating power in the MSB is
reduced from 22.4% to nearly zero, with the ITBS
MTBF increased from 0.31 to 0.34 years.
4.3.2. TM2: Moored surface buoy firmware
improvement
Fig 7 shows the portion of the failure trees, where
the failure of data communication is between the
MSB and the satellite terminal, with significant contribution owing to the firmware failure in the MSB
processor. The failure of communications results in
direct intervention through maintenance, which is
exorbitant. The failure is reduced by incorporating
a three-level software-based watchdog timer. This
feature enables the MSB processor system to reboot
during the processor’s prolonged hang-ups for more
than 10mins continuously, with a pre-programmed
periodicity of 10min, 3hr and 10 days. The two-way
communication feature is further enhanced by
including a forced restart facility from the MCC.
Further, the processor is enhanced by customisation by means of which the hardware FIT is reduced
by a factor of 10%. Through this modification, the
PoF of data transfer and communication failure
between the MSB and MCC is reduced from 24.49%
to 2.8% in a one-year period, with the ITBS MTBF
increased from 0.34 to 0.37 years. The improvements are represented in Fig 10.
4.3.3. TM3: Network redundancy
Fig 8 shows the portion of the failure trees for the
tsunami data receipt at the NIOT-MCC. The communication was based on the modem-to-modem
principle that resulted in long queuing of data in
the sending modem, which increased the latency
time and delayed the valuable decision-making process and time during a likely tsunami event. The
solution to this failure scenario is found by means of
email communication, wherein the data from the
modem is routed to both the NIOT and INCOIS
mail servers through the public networking system.
This is to ensure redundancy in the receipt of data
in the NIOT-MCC and INCOIS centre. In case of
any failure from the public cabled network link failure at INCOIS, the data from the NIOT-MCC will
be fetched by the INCOIS centre, through peer-topeer very small aperture terminal communication.
Based on the improvement, the PoF of data transfer
and communication failure is reduced to 44.03%,
with the ITBS MTBF increased from 0.37 to 0.87
years, and the same is shown in Fig 11. Further
improvements, such as utilising FTP to reduce the
263
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
Power source failure
in MCB
And39
U(8760h)=1.6187E-13
mission-time=8.76E3
Power failure owing to
lead acid batteries
and solar panel
Lithium battery bank
failure
Or35
U(8760h)=0.224
mission-time=8.76E3
KOutOfN40
8/8
U(8760h)=7.2268E-13
mission-time=8.76E3
8
Power
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
80
81
84
85
Evt80
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt81
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt84
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt85
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
Lithium thionyl
chloride batteries
failure
82
83
86
87
Evt82
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt83
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt86
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Evt87
exponential LiBattery
U(8760h)=3.0365E-2
mission-time=8.76E3
Fig 9: Power system failure reduction by incorporating lithium thionyl battery storage
dependency on public networks and mail server
and using redundant satellite and LES communications, are under investigation.
4.3.4. TM4: Buoy position watch
In order to have a better acoustic communication,
the BPR is deployed within 300m of the MSB. Failure of the MSB mooring results in the loss of MSB
at the location and renders the node unavailable
during an event. One of the classic cases was the
event reported on 12 April 2012 (Srinivasa Kumar
et al., 2012): the MSB of the node in location 7
N/87 E got separated from the mooring owing to
the act of unknown elements and was identified
during routine maintenance on 5 December 2012.
The retrieved BPR was found to have recorded an
event that occurred on 12 April 2012, but was not
reported to the MCC because the MSB was not in
place. Such a situation renders the specific node
unavailable for the system. To overcome the impact
264
of such an undesirable situation, a buoy position
watch system is implemented. This system transmits
the buoy position to the NIOT-MCC every hour.
Fig 7 shows the fault trees, indicating the PoF of
the MSB owing to mooring failure to be 26.84%.
With the modification implemented, in the case when
a mooring failure is reported, action will be taken to
track the parted buoy, retrieving it for servicing and
reinstallation, based on the ship availability. Mooring
failures’ data are being analysed and further improvements are underway. Fig 12 shows the buoy position
information continuously logged in the NIOT-MCC.
4.3.5. Consolidated results of technical maturity
Table 3 summarises the improvements in the ITBS
from the base case to the matured case. By means
of these technical improvements, the PoF of a single
node reduces from 95.95% to 68.3% in a one-year
period, and hence, the MTBF increases from 0.32
to 0.87 years for the systems installed in the Bay of
Underwater Technology Vol. 32, No. 4, 2015
SBS
communication
(DAS) failure
Or18
U(8760h)=2.8026E-2
mission-time=8.76E3
Surface buoy data
acquisition board
failure
Surface buoy data
acquisition board
failure
Surface buoy power
board failure
35
36
37
Evt35
exponential SBSdataboard
U(8760h)=2.5938E-2
mission-time=8.76E3
Evt36
exponential SBSprocessorboard
U(8760h)=5.1671E-4
mission-time=8.76E3
Interface cable from
battery to DAS failure
Power source failure
in MSB
Evt37
exponential SBSpowerboard
U(8760h)=7.5308E-4
mission-time=8.76E3
MSB software failure
And39
69
U(8760h)=1.6187E-13
mission-time=8.76E3
Evt69
Power
exponential uwpoercables
U(8760h)=8.7562E-4
mission-time=8.76E3
75
Evt75
exponential 2.85E-15
U(8760h)=2.4966E-11
mission-time=8.76E3
Fig 10: MSB data acquisition software failure reduction with firmware improvements
Mission Control
Centre (MCC)
failure
And77
U(8760h)=0.4403
mission-time=8.76E3
Mission Control
Centre at INCOIS
failure
Mission Control
Centre at NIOT
failure
Or24
U(8760h)=0.8819
mission-time=8.76E3
MCC server failure
MCC power input
failure
Or10
U(8760h)=4.8471E-3
mission-time=8.76E3
MCC power
Or62
U(8760h)=0.4993
mission-time=8.76E3
MCC power input
failure
Or23
U(8760h)=0.1813
mission-time=8.76E3
MCC server
MCC server failure
MCC server
MCC power
Data failure from
NIOT server station
Or38
U(8760h)=0.8551
mission-time=8.76E3
Data
Or61
U(8760h)=0.1813
mission-time=8.76E3
Or48
U(8760h)=4.8471E-3
mission-time=8.76E3
Data failure from
INCOIS server
station
Or76
U(8760h)=0.3858
mission-time=8.76E3
Data
Fig 11: Improvements in MSB-MCC data reception performance
265
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
Fig 12: Buoy positions watch system to monitor possible mooring failures (reproduced with
permission from NIOT)
Table 3: Summary of improvements from the base to the matured case
L1
L2
L3
L4
L5
Data reception
in INCOIS
BPR system
Moored
surface buoy
Data transmission
to Inmarsat
Power failure
Sensor arm/
antenna mast
Surface buoy
DAS data failure
MSB data
acquisition and
processing
boards
MSB processor
software
Positioning of
surface buoy
Data from
acoustic receiver
Tsunami
warning signal
reception
Reception input
data
Reception
server data
failure
Primary link
Secondary link
Reception
power failure
Reception server
266
PoF (%) in
base case
PoF (%) in
matured case
95.95
68.3
5.25
63.83
5.25
40.22
39.61
22.24
22.34
22.12
Negligible
22.12
24.49
2.8
2.72
2.72
22.09
Negligible
20.84
20.84
0.08
0.08
88.2
44.03
85.52
44.03
85.44
49.93
99.9
85.4
0.05
61.85
61.85
Negligible
18.13
18.13
Underwater Technology Vol. 32, No. 4, 2015
Bengal location. A similar analysis is done for the
system installed in the Arabian Sea, with the respective field failure data recorded in FRACAS, and the
MTBF has increased to 1.43 years over the period
from 2008 to 2013.
5. Availability and maintainability
The availability of the system is a critical aspect, as
the system has to be available to detect and report
an event during the entire operating period. Efforts
have been taken by NOAA to maintain the availability of the DART network at the highest possible level
(Meinig et al., 2005) since its inception in 2006,
and the return of experiences is taken into account
in realising the ITBS. The ITBS with one offshore
node is thus identified to have an MTBF of 0.87
years. Increased number of nodes could increase
the availability of the system, while in demand.
Availability in % = MTBF / MTBF + MTTR
(2)
When a failure is reported from a single offshore
node and with the mean time to repair (MTTR) of
seven days, the availability of the system with the
MTBF of 0.87 years is computed to be 97.84%. With
Table 4: Influence of more offshore nodes in ITBS
Configuration
Bay of Bengal
1 node
2 node
3 node
4 node
Arabian Sea
1 node
PoF
MTBF
Availability*
68.3%
54.55%
48.59%
46.01%
0.87
1.27
1.50
1.62
97.84%
98.51%
98.74%
98.83%
50.28%
1.43
98.68%
*Availability in % = MTBF / MTBF + MTTR, MTBF and
MTTR in hours, MTTR taken as 7 days
the objective of having a higher MTBF and availability, so as to have a reduced maintenance interval,
the ITBS with more number of offshore nodes is
analysed. Table 4 shows the results of the analysis.
Fig 13 shows the ITBS configuration with four
offshore nodes in the Bay of Bengal. The gate 157
in Fig 13 indicates that the failure of the fourth
node leads to complete system failure. Based on
the number of nodes in operation and the MTBF,
a maintenance plan has evolved. Depending on
the MTBF and the availability targets computed
with the recorded data, a minimum of two nodes
are required in the Bay of Bengal and one node in
Tsunami warning
system failure
Or25
U(8760h)=0.4601
mission-time=8.76E3
Offshore nodes
failure for tsunami
data
KOutOfN157
4
4/4
U(8760h)=3.5339E-2
mission-time=8.76E3
Mission Control
Centre (MCC)
failure
And77
U(8760h)=0.4403
mission-time=8.76E3
MCC
Offshore node 1
failure
Offshore node 3
failure
Or78
U(8760h)=0.4336
mission-time=8.76E3
Offshore1
Or235
U(8760h)=0.4336
mission-time=8.76E3
Offshore3
Offshore node 2
failure
Offshore node 4
failure
Or313
U(8760h)=0.4336
mission-time=8.76E3
Or156
U(8760h)=0.4336
mission-time=8.76E3
Offshore2
Offshore4
Fig 13: Trees indicating PoF with ITBS operating with four nodes
267
Venkatesan et al. Assessment of the reliability of the Indian tsunami buoy system
Table 5: PFD and SIL levels. 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
2
3
4
10–1 to 10–2
10–2 to 10–3
10–3 to 10–4
10–4 to 10–5
the Arabian Sea. However, based on the battery
replacement needs, the maintenance interval for
the offshore nodes will be one year. Two independent nodes, one in the Bay of Bengal and another one
in the Arabian Sea, operated and maintained by
INCOIS with the proven DART system will serve as
a standby, which is not considered in this analysis.
6. Safety integrity level
IEC 61508/11 is a standard (Smith and Simpson, 2004;
Yoshimura and Sato, 2008; IEC, 2000; Bukowski,
2001) that is essentially a framework for implementing instrumented safety systems, using the principles of the safety life cycle and safety integrity level
concepts. Protection systems need to perform their
intended operations on demand. PoF is the unavailability of a safety system on demand. If a demand
occurs after a time, the probability that the system
has already failed is the probability of failure on
demand (PFD). It is also defined as the ratio
between the tolerable frequency of the accident to
the frequency of the accident with no protection
(IEC, 2000). The SIL defines the degree of safety
protection required by the process and, consecutively, the safety reliability of the system necessary
to achieve the function. SIL has four levels, 1 to 4,
with the higher number meaning the safer the system. Table 5 describes the various SIL levels with
the corresponding PFD.
Based on the IEC 61508, the SIL requirements
are computed taking into consideration the risk consequence, alternative safety instrumented function
(SIF) in place, human occupancy and the demand
rate for the SIF. This SIL determination methodology is applied in process and in marine industries (Vedachalam et al., 2014a,b; 2013). A similar
approach is adopted here to determine the SIL
requirements for the ITBS.
6.1. Determination of the required safety integrity
level for the Indian tsunami buoy system
Avoidance parameter (P ): Based on the availability
or unavailability of an alternative SIF, the parameter is assigned a value of 0 or 1, respectively. In this
case, the value is taken as 1, as there is no alternative SIF in place.
268
Occupancy parameter (F ): Based on the human
occupancy, the parameter takes the values of 2, 1
and 0 corresponding to continuous, occasional and
rare human presence in the mission. In this case,
the value is 2.
Demand rate parameter (W ): A 100-year-old historical database indicates that an average of five
tsunami events per year occurs in the Pacific (Lockridge, 1988; Jin and Bin, 2011). Based on the NIOT
return of experiences with the ITBS system installed
in the Bay of Bengal and Arabian Sea, the number
of events during the period 2007 to 2014 was found
to be more than 14 (Srinivasa Kumar et al., 2012),
depending on which the system demand rate factor
is identified to be more than one event per year.
Thus, the demand rate factor W (based on Table 6)
is computed to be 9 for the Indian Ocean conditions. Table 7 shows the values taken as the input
for the risk consequence parameters.
Having computed the values of P, F and W, i.e.
12 (1 + 2 + 9), the summed up values are plotted
against the consequence factor in the risk graph
matrix shown in Table 8, so as to obtain the required
level of SIL. From the risk graph matrix, it is identified that the operating ITBS should comply with
SIL 4 level, which is equivalent to the requirements
considered for a nuclear power plant (IEEE, 1984).
Fig 14 shows the SIL computed using the TOTAL
Table 6: Factors for the SIF demand rate
Demand rate
W9
W8
W7
W6
W5
W4
W3
Factor (W)
Often >1/year
Frequent 1/1–3 year
Likely 1/ 3–10 year
Probable 1/10–30 year
Occasional 1/30–100 year
Remote 1/100–300 year
Improbable 1/300–1,000
year
9*
8
7
6
5
4
3
* Value considered for this case
Table 7: Risk level assignment data
Personnel Environment Financial
health
Consequence* Catastrophic Catastrophic Catastrophic
* Catastrophic, extensive, serious, considerable, marginal,
negligible
Underwater Technology Vol. 32, No. 4, 2015
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
Extensive
Serious
Considerable
Marginal
Negligible
F
E
D
C
B
A
NR
NR
NR
NR
NR
NR
SIL1
NR
NR
NR
NR
NR
SIL2
SIL1
NR
NR
NR
NR
IL3
SIL2
SIL1
NR
NR
NR
SIL4
SIL3
SIL2
SIL1
NR
NR
> SIL4
SIL4
SIL3
SIL2
SIL1
NR
NR – not required; SIL – safety integrity level
Probability of failure on demand
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
SIL3
SIL4
0
1
2
3
4
5
6
7
8
9
10
11
Hour(s)
Fig 14: Achieved SIL4 for ITBS with one node in operation
GRIF SIL module based on the PoF results obtained
from the fault tree analysis. It is seen that the ITBS
configuration with one offshore node in operation
should have a proof test interval (PTI) of around
10 hours, so as to confirm it to SIL 4 requirements,
independent of the response during a tsunami event.
This requires system and operational health to be
verified and confirmed every 10 hours to the MCC.
However, considering the disastrous nature of the
event, SIL levels greater than SIL 4 are required.
The proof test data involve the transmission of
system health parameters, including battery status,
sea water column and buoy position information
from each offshore node to the MCC every hour.
This ensures that each node complies with the
highest standards and levels of safety.
7. Summary and conclusion
The present paper discusses the reliability of the
ITBS installed in the Bay of Bengal and the Arabian
Sea. The influence of technical maturation by incorporating an effective and reliable energy storage
system, firmware improvements and network redundancy is quantified with computations. A reliability
analysis carried out on the base case and matured
systems indicates that the MTBF of a single offshore
node in the Bay of Bengal communicating to the
NIOT-MCC has increased from 0.3 years in 2007 to
0.9 years in 2013. It is identified that, with the operational, matured ITBS, it is possible to have an
MTBF of more than 1.6 years and 1.4 years with
four and one offshore node/s in the Bay of Bengal
and Arabian Sea, respectively. However, based on the
battery replenishment needs and sensor maintenance needs, the maintainability schedule is arrived
at as one year, which could provide an availability of
more than 98.5%. Based on the IEC-based HSE
analysis, it is identified that the ITBS system needs
to comply with SIL 4 levels of reliability.
The safety analysis reveals that a configuration
with one offshore node reporting every 10 hours to
the mission control centre, it is possible to attain
SIL levels better than the most stringent SIL4. This
complies with the present reporting duration of
every hour, which is independent of the ITBS operation during the actual tsunami event. The present
study has given the confidence on the ITBS reliable
support to India’s Tsunami Early Warning System
in the light of societal importance. In view of the
uniqueness and considering the relevance and
importance to the coastal community, this reliability engineering model could be adapted to other
regions with the relevant data.
Acknowledgements
The authors thank the Ministry of Earth Sciences,
Government of India for funding this project. The
authors are indebted to the Directors of National
Centre for Antarctic and Ocean Research (NCAOR),
Goa and INCOIS, Hyderabad, for their support.
The authors also thank the staff of the Ocean Observation Systems (OOS) group, Vessel Management
Cell of the NIOT, and the ship staff for their excellent help and support on-board.
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