Tech Briefs Magazine - November 2023 - 22

Electronics
Finalist
AI-Deep Learning and LowCost
Passive RF Wireless
Vibratory Strain Sensors
Reamonn Soto, Taofeek Orekantorekan,
Azryana Soto
Sensatek
Daytona Beach, FL
O
perating revenue-generating assets
in harsh environments
poses safety and financial risks. For example, catastrophic
wind turbine blade failures are costing $1.5 billion a
year to fleet owners and operators across 700,000 blades in
operation globally (Wind Power Engineering, 2018), and
safety incidents have increased by 22.3 percent on wind
farms (Global Offshore Wind Health and Safety Organization).
Sensatek discovered that passive Radio Frequency-based
(RF) wireless dynamic strain sensors are a promising
technology to help safely watch assets used in harsh
environments, such as trending crack growth on wind turbine
blades to help eliminate blade failures.
According to the team, " Some of the challenges we faced
during design and development included managing customer
expectations with some of the wind power companies in
the face of technical constraints. Also, we experienced challenges
in not being able to afford the ideal software packages
to conduct CFD and FEA simulations. Nevertheless, we got
really creative. "
The wireless RF vibratory strain sensors are passively powered,
not requiring batteries, and are applied to the structure
of interest by using a bonding method. A wireless gateway is
installed nearby and is used to communicate with multiple
wireless passive RF sensors and sends the data remotely to
the cloud. The software application uses the data along with
AI-deep learning to prioritize maintenance based on vibratory
strain responses of the structure being monitored.
The passive RF vibratory strain sensor is based on a microstrip
patch antenna that consists of a radiation patch,
a dielectric substrate, and a ground plane. The microstrip
patch antenna behaves as an antenna and a sensor whose
frequency response depends on its geometry, whereas its
ground plane contributes mainly to the response of the antenna
sensor.
As the structure experiences changes in strain stress, it
causes a change in the radiation pattern of the microstrip
patch antenna sensor. A miniaturized frequency-modulated
continuous wave (FMCW) radar circuit is coupled with the
22
sensor to enable dynamic interrogation of the antenna sensor
enabling vibratory strain data to be generated based on the
strain-stress profile of the structure the sensor is coupled to.
Competing methods such as drone inspections, wired
strain gauges, acoustic monitoring, accelerometers, and vibration
sensors are also used in structural monitoring applications.
Those methods are expensive, results are often misdiagnosed,
and require much maintenance to ensure the
sensors are operational.
According to the Sensatek team, " Our solution is more effective
because it monitors the damage more directly on
parts such as wind turbine blades because sensors are installed
in situ in hard-to-reach areas without requiring batteries
or lengthy cables, further eliminating costs. Furthermore,
our sensors provide information on the strain, vibration
frequency, and load of the part being monitored, enabling a
rich data stream that makes the AI-deep learning algorithm
more robust to prioritize maintenance and safely mitigate
catastrophic failures. In addition, they are manufactured for
5x less than the cost of competing solutions. "
This technology has many applications besides wind turbines
and includes airframes, gas turbines, space launch vehicles,
electric machines, bridges, and natural gas pipelines,
presenting opportunities within the industrial, aerospace,
and automotive markets. Sensatek is already seeing interest
from GE, Lockheed Martin, Siemens Energy, Pratt & Whitney,
and large utilities in piloting the technology.
" We are currently working with wind turbine manufacturers
such as Siemens Gamesa to install sensors on some of the
biggest wind turbines, especially for use on offshore wind
farms, " said Reamonn Soto, Founder and CEO, Sensatek. " We
are agile, working on product development in real-time while
talking with customers. Hopefully, in the next 30 days we will
have a pilot executed. There are seven other wind farm companies
currently in our pipeline that are also identifying wind
farms to test on. "
For more information, visit https://contest.techbriefs.
com/2023/entries/12539.
www.techbriefs.com
Tech Briefs, November 2023
http://www.createthefuturecontest.com https://contest.techbriefs.com/2023/entries/12539 http://www.techbriefs.com
Tech Briefs Magazine - November 2023

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