How do you safely send a human being to inspect a petroleum refinery flare stack – one that operates at hundreds of degrees and requires negotiating a high-risk vertical climb? The answer is you don’t. Yet this important work is at the heart of a $40 billion industry1 — one that Avitas Systems is disrupting with AI and NVIDIA DGX.
The Cost of Staying Safe
In many industries, such as oil and gas, transportation and energy, ensuring asset and facility uptime, as well as safety and regulatory compliance, is non-negotiable. Companies can spend upward of $100 million1 every year on industrial inspections, which in turn can drive five times that amount in maintenance costs.
Many companies employ traditional time-based inspection practices with the expectation that choosing the right inspection interval will identify a problem before the probability of failure is unacceptably high. This approach often incurs exorbitant, but necessary cost, while bearing the risk of missing critical defects that escape the inspection window.
Using Deep Learning to Power Risk-Based Inspection
In many sectors, the shift away from time-based inspection has begun. Risk-based inspection (RBI) involves extensive data and probability calculations, combined with detailed assessments of the consequences of component failure.
Information fuels RBI, and a new generation of drones and other unmanned robotic technologies is helping collect the sensor and video data that can drive the calculations leading to more intelligent maintenance scheduling. GPU-powered deep learning combined with advanced analytics and robotic inspection is helping Avitas Systems deliver a service that more accurately predicts when maintenance is required.
At the heart of this process is NVIDIA DGX-1, the AI supercomputing platform helping Avitas Systems learn and train from immense volumes of captured data. Using computer vision techniques, Avitas Systems learns to detect faults and create heat maps of target areas and components to repair or replace, prioritizing based on calculated risk. This trained model is then optimized for field deployment, where an array of drones and other unmanned robots equipped with sensors and cameras collect data and video footage of sites being inspected.
Bringing the Data Center to the Field
The story doesn’t end on a DGX-1 in the data center. The industrial sites being inspected are sometimes located near the “edge of civilization” — places far removed from robust networking infrastructure. The voluminous flow of data returning from field drones and robots is often too large to be fed back to the data center for deep learning inferencing.
To address this challenge, Avitas Systems flipped conventional thinking and brought the data center to the field — in the form of the NVIDIA DGX Station. It packs the power of hundreds of CPUs (literally racks of x86 data center servers) into a compact form factor that sips electricity by comparison. Avitas Systems uses DGX Station for inferencing on the data closest to where that data is being created, while enabling model refinement as the data comes in.
Robots That Get Smarter, Saving Companies Money, Saving the Environment
With every second of captured data comes the opportunity to leverage deep learning to improve the model, retrain it on the latest information, and increase the speed and efficacy of robotic inspection across all field sites. In this way, Avitas Systems has created a complete lifecycle of deep learning value that continuously evolves and improves service to customers, powered by the constantly growing footprint of sensor and video data.
Avitas Systems estimates this service reduces industrial inspection costs by as much as 25 percent, while also ensuring that industrial sites are properly maintained to avoid escalating emissions and damage to the environment.
Check out our infographic that tells the Avitas Systems story.
Continue the Conversation – Watch the Webinar
Watch this webinar featuring Ser Nam Lim, director of Advanced Analytics and Machine Learning at Avitas Systems, who discusses how his company built a complete, end-to-end service powered by NVIDIA DGX.
To request a call from an NVIDIA representative to discuss NVIDIA DGX systems, click here.