Condition-based Maintenance
Assembly Line
How NOV solved critical oilfield operations using Databricks Data Intelligence Platform
NOV is a global oilfield services corporation. In order to better achieve scale and achieve data driven decisions across their operations, NOV aspired to create a data command center involving a comprehensive approach to ingest and seamlessly integrate real-time streaming data with historical batch data to provide a holistic overview of well site activities.They have implemented advanced data analytics and visualization tools to make sure that operators can promptly identify anomalies, potential issues, and trends from the well data.
Rigs generate over 1 billion rows of data per day from 30,000+ sensors streaming at 1 Hz or higher. This sensor data streams seamlessly into central repositories such as Aveva PI, formerly known as OSIsoft, yet corporate data and equipment details remain siloed, needing duplicated efforts due to limited data access. A robust model deployment pipeline significantly improves data-driven optimization. Modernizing to a unified, scalable data environment is critical for unlocking data-driven insights.
Implementing Databricks on AWS has proven instrumental in advancing CBM strategies within the realm of drilling equipment, leading to substantial improvements in the supply chain management of such crucial assets. By harnessing the power of data analytics and ML on the Databricks Data Intelligence Platform organizations can efficiently monitor the health and performance of drilling equipment in real-time.
Fully automated, physics-based analysis of SCADA temperature signals for Drivetrain Monitoring
The SkySpecs Performance team has demonstrated that physics-based models can be used to model the thermal response of components inside the wind turbine with full transparency, low complexity, and high accuracy. By using this approach, a number of faults can be detected across a wind turbine portfolio, including gearbox, main bearing, and generator damage.
The rate of change of body temperature is then calculated using a thermal inertia coefficient, calculated for each individual turbine. All coefficients are trained using reference datasets from relatively short time periods, and used to predict future temperature responses of the same components. Prediction and measurement are finally compared to produce residual values, which in turn were used to detect the presence of component faults.
Drone Powerline Inspection
While the quest for operational efficiency is driving drone powerline inspection adoption, the true innovation lies in what happens after the data capture with AI/ML data processing. To realize the full benefits of drone powerline inspection, computer vision AI/ML algorithms are utilized to automatically analyze the massive quantity of raw visual image data to classify assets and identify predetermined defect conditions. This dramatically improves the efficiency of drone powerline inspection programs by flagging images with detected issues for further MI (maintenance inspector) review, saving time and reducing inspector fatigue.
Machine learning is another critical element of inspection automation. When the AI model is retrained with additional annotated imagery showing new environmental conditions and equipment, it learns, improving its algorithms and becoming more adept at classifying assets and identifying failure conditions. This enables the system to become more efficient over time, adapting to new patterns of wear, new component types and evolving changes to the transmission network.
🪱🤖 GE Develops Worm-Inspired Robot For On-Wing Engine Inspections
Resembling an inchworm, the Sensiworm (Soft ElectroNics Skin-Innervated Robotic Worm) uses untethered soft robotics technology to move easily through the nooks, crannies and curves of jet engine parts to detect defects and corrosion. The robot is also able to measure the thickness of an engine’s thermal barrier coatings.
Developed in partnership with SEMI Flex Tech, Binghamton University and UES, Inc., Sensiworm is controlled by an operator using a device that GE says is similar to a gaming controller and can be programmed to follow specific paths. “It has a sticky, suction-like bottom that enables it to climb and adhere to steep surfaces. Also, because the robot is very soft and compliant, it won’t harm any surfaces or cause any damage during an inspection,” says a spokesperson for GE.
According to GE, Sensiworm could reduce unnecessary engine removals and downtime, enabling faster turnarounds. Although Sensiworm is currently focused on engine inspections, Trivedi says the OEM is developing new capabilities that would enable the robot to execute repairs once it finds a defect.
The Future of Oil and Gas Inspection Software
The very nature of oil and gas operations makes assets susceptible to corrosion. Regular inspections help detect early signs of corrosion, thereby preventing potential leaks or failures. Modern technologies, such as drones and visual AI, have revolutionized this aspect, allowing for more detailed, quicker, and safer inspections.
Optelos stands out as a quintessential example of this type, merging the capabilities of the aforementioned software types into one cohesive solution. From managing visual data from UAVs to operationalizing visual AI for corrosion inspections and creating 3D digital twins, integrated platforms provide a holistic approach to oil and gas inspections.
🛣️ America’s Bridges, Factories and Highways Are in Dire Need of Repairs. Bring in the Robots.
These days, Shell is able to keep the plant running, and keep repair personnel on the ground and at a safe distance as they operate wall-climbing robots that inspect things like steel holding tanks at millimeter resolution, says Steven Treviño, a robotics engineer at Shell. Using a variety of sensors, the robots can look for both corrosion and cracking. This helps the team shorten the list of things they have to take care of when a full shutdown occurs. The magnetic wall climbers Shell is using are made by a Pittsburgh-based startup called, appropriately, Gecko Robotics. After testing the Gecko robots at Geismar, Shell plans to expand their use to offshore facilities.
“There are hundreds of types of corrosion,” says Jake Loosararian, CEO of Gecko Robotics, “and we’ve been developing technology and software to analyze what kind of damage is happening.” Gecko began as a robotics company, but has since expanded into creating software to process the data its robots gather. The startup makes systems that are now used to track more than 60,000 assets across the globe, including power plants, pipelines, oil refineries, dams, U.S. Navy vessels and other military equipment.
When it comes to inspections, “often the data you need is literally in plain sight, it’s just hard to collect it,” says Bry, of Skydio.