FEATURE AI & Machine Learningļ¹
Fotech increases accuracy of sensing with Nvidia computing
T
echnology company Fotech has used Nvidia’s GPU-accelerated computing to process exceptionally high volumes of data needed for its distributed acoustic sensing (DAS) technology.
Fotech’s innovative DAS technology turns a fi bre optic cable into thousands of sensors, detecting any disturbances along the length of the fi bre. The technology sends thousands of pulses of light along the fi bre optic cable every second and monitors the fi ne pattern of light refl ected back. When acoustic or vibrational energy – such as that created by digging, walking or driving – creates a strain on the optical fi bre, this changes the refl ected light pattern. The technology uses advanced algorithms and processing techniques to analyse these changes to identify and categorise the disturbances. Each type of disturbance has its own signature, and the technology can tell an operator what happened, where, when and how – all in real time. This provides valuable information for various applications in security, pipeline integrity, power infrastructure and telecommunications, among many others. However, one of the key challenges Fotech faces is ensuring the high accuracy of its technology, so customers have full confi dence in the system.
10 October 2022 | Automation
“We need to process up to 600MB of data per second, an exceptionally large volume which would take too long to transfer to the cloud for our customers to get the insights they need, and would be too expensive,” said Steve Cammish, Fotech’s Chief Technology Officer. “In order to detect with the sensitivity required, and cover the distance of fibre under monitoring, we require intense edge computing.” Fotech previously used Nvidia GPUs
but recently upgraded to the Nvidia Jetson Edge AI platform.
“The Nvidia Jetson Edge platform and
CUDA libraries are so important to us, as they process data in parallel, which helps us to detect different types of events, such as spade strikes or footfall. Once we know the events, we use much lower data rates, carrying out processing either on the device or in the cloud, to create alarms,” added Cammish. In addition, the accuracy of the events has been improved through implementing new data flows for machine learning to improve detection. “The current approach requires significant manual tuning of thresholds and frequency bands to classify disruptions like walking, digging and vehicle movements, all of which takes time,” said Cammish.
Hence, Fotech used Nvidia’s
TensorRT, a software development kit for high-performance deep-learning inference. “We collect data, we label it, process it and create trained models that we test before we deploy devices on live sites,” continued Cammish. “Using Nvidia TensorRT allows us to automatically execute models that are constantly improving as we collect more data. We are now able to use machine- learning techniques to look for new patterns across the time series data sets and provide new insights for our customers.” With machine learning, data can be tuned and the analytics configured more quickly. As such, it is quicker and easier to commission a more accurate system in the field. “Without Nvidia computing technology, Fotech would not be able to process all the data at the edge quickly enough to detect events and create insights and incidence alarms with the high confidence that our customers have come to expect from us,” said Cammish.
CONTACT:
Fotech
www.fotech.com
automationmagazine.co.uk
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