NTU Singapore start-up commercialises AI algorithm that can detect leaks instantly in gas pipeline networks

A sensor network powered by an artificial intelligence (AI)
algorithm developed by scientists fromNanyang
Technological University, Singapore (NTU Singapore) 
accurately detect, in real-time, gas leaks and unwanted water
seepage into gas pipeline networks. 
Successful in field trials conducted on Singapore’s gas
pipeline networks, the algorithm has been patented and spun off
into a start-up named Vigti, which is now
commercialising the technology. It has recently raised early
start-up funding from Artesian Capital and Brinc, Hong Kong.
The NTU start-up is incubated by the
University’s EcoLabs Centre of Innovation for
, a national centre launched in April 2019 to help
small and medium-sized enterprises (SMEs) and start-ups innovate,
grow and thrive in the energy sector.
A smart warning system that can detect gas leaks and broken
gas pipes in real-time has been a long-term goal for the public
utility industry, as the current industry best practice for
inspecting pipes is for workers to undertake manual surveillance at
regular intervals. 
While big leaks can be easily detected via conventional
sensors as the gas volume and pressure differences will fluctuate
sharply in the pipe networks, small leaks are much harder to
In 2014, the Energy Market Authority of Singapore (EMA)
awarded a grant to NTU researchers led by Dr Justin
, then an associate professor at the School of
Electrical & Electronic Engineering, to develop an anomaly
identification software for low-pressure pipeline networks.
Over a four-year period starting from 2015, the NTU
researchers developed, deployed and tested their AI solution on
certain segments of the local city gas network in Singapore over
six months, which was shown to be successful in detecting all
tested types of anomalies.
“We have designed novel AI algorithms, trained on a massive
amount of field data, to identify anomalies such as leaks, bursts
and water ingress, which can aid energy companies to better manage
their pipe networks,” added Dr Dauwels, who is now the AI Advisor
of Vigti.
The EMA funded project concluded in 2019 after the successful
field trials and Vigti was then formed to continue developing the
innovation and bring it to the global market.
Chief Executive Officer of Vigti, Mr Ishaan
, said: “We aim to reduce the methane emissions in
the global gas supply chain to a minimum, with our early detection
system, helping companies to save costs while protecting lives. Our
mission is to create a safe, smart and a sustainable world, one
pipeline at a time.”
Professor Subodh Mhaisalkar, Executive Director of the
Energy Research Institute @ NTU (ERIAN)
Governing Board Member of EcoLabs, said
Vigti’s technology is a prime example of an NTU innovation going
from lab to market.
“With ageing infrastructure and rising gas leaks around the
world, Vigti’s solution is well-positioned to solve a global
problem, mitigating gas emissions and leaks that impact climate
change and pose a potential threat to the well-being of
communities. At NTU EcoLabs, we have pooled together expertise and
the funding for Vigti, which enabled the pilot-scale testing of the
technology, paving the way for actual market adoption.”
Conventional sensors vs AI-based
While within a typical gas network there are sensors installed
at regulator points that can detect major fluctuation in the
network and calculate the Unaccounted-for-Gas (UFG) loss, small
leaks and cracks can escape notice and thus must be manually
With the conventional threshold-based approach, leaks can only
be detected if the pressure drop due to the leak is higher than the
pressure variation of the network during normal operation. If it is
lower than the pressure variation, the leaks will be very hard to
detect unless the pipes are inspected manually. 
The cumulative loss of all the small leaks for major companies
across the world is estimated between 1.5 to 3 per cent of total
gas consumption. 
Total natural gas consumption worldwide is estimated to be 3.9
trillion cubic metres as of 2019, thus even a 1 per cent loss would
mean some 39 billion cubic metres globally (10 times the total
consumption of natural gas of Singapore in 2017).
Leveraging machine learning and AI
To tackle these issues, the NTU team performed various
computational simulations to understand the leak and water ingress
phenomena in the city’s natural gas distribution networks.
A variety of sensors that can measure pressure, flow,
temperature and vibration, were deployed and the resulting signals
associated with the anomalies in the network’s pipes were
analysed. This process established unique ‘signatures’ within
the sensor data for each anomaly.
Using machine learning and AI, the team then developed a
software algorithm that is extremely sensitive in detecting
anomalies by matching these unique signatures within the sensor
data that is routinely monitored.  
During the field trial, a total of 16 pressure sensors and 4
flow sensors of various types were deployed at the riser, service
line and main line, across three different locations. Data was then
analysed at each location and leak and water ingress tests were
also performed at these sites. 
At the end of the project, a test was done to establish the
effectiveness of NTU’s AI comprising 13 different anomaly tests.
All 13 were successfully identified by the algorithm as leaks,
along with the nearest sensor location and the time duration of
these leaks. 
Originally published by

Lester Kok, Assistant Director,Corporate Communications
Office, Email: lesterkok@ntu.edu.sg | January 20, 2021