The Advantages of Automating Visual Asset Inspection

Optimal asset functioning is always the goal. That’s why global utilities spend millions of dollars per year on vegetation management and asset inspection programs. Yet new remote sensing technology, surveying methods, and AI-driven asset condition assessment solutions provide a better way to plan and analyze asset health. This blog will examine the benefits of a digital upgrade from legacy inspection and risk management programs.

Digital, automated visual intelligence offers an unlimited view of different assets, asset types, asset components, and asset anomalies however far-flung in the service territory. Replacing land-based asset inspections with helicopters, satellites, drones or other aerial methods is faster, cheaper, and safer. Plus, comprehensive access to images, videos, thermal, hyperspectral and multispectral information can improve the accuracy of the system of record.

GE Vernova Grid Software’s vegetation inspection technology can:

With digital visual data inspection and analytics, inspection planning and management benefit while the utility reduces outages, increases compliance, improves safety and lowers the probability of catastrophic events.

This blog will examine the benefits of a digital upgrade from legacy inspection and risk management programs. We will review a three-step process to follow that can potentially reduced outages by 30% while also reducing vegetation management costs by 20%.

AI enhanced asset inspection ingests the data across all layers of the utility to recognize asset condition and predict defects. With such large amounts of data to process, gaining a compelling view of the network can be challenging. But with computer vision powered by artificial intelligence (AI), the inspection platform can ingest all major filetypes (RGB, LiDAR, Hyperspectral, Multispectral, FLIR) to provide comprehensive insights.

Legacy survey models (land-based line patrol) are very expensive, time consuming and can have unacceptable liability risk. Plus, data collected using this approach can have variation and integrity issues due to inconsistent perspectives, focal inaccuracies and distance from the target objects. However, AI-powered visual inspection analysis normalizes the data so that asset managers can optimize their overall condition-based asset risk management programs.

With automated asset inspection data management, the utility can efficiently centralize and process raw data, associate images (or point clouds) with specific assets and extract the risk classifications. This helps reduce cost and complexity, as discussed next.

Maintenance, inspection and asset management are the largest OPEX budget lines for utilities. With automated asset inspection, the utility can immediately cut costs associated with traditional inspection approaches. Additionally, the powerful asset insights gained benefit change detection, storm damage assessment and hotspots detection. All of which help the utility to detect and mitigate failures early and reduce maintenance and mobilization costs.

Interoperability with Asset Performance Management (APM), Geographic Information System (GIS), and Enterprise Resource Planning (ERP) systems also supports reductions in time, complexity, and number of errors in data transfer, integration and visualization. Further, visual data management and AI analytics allow the utility to remove subjectivity and standardize methodologies to improve speed of defect detection, expedite incident response and, simultaneously, increase customer satisfaction.

Automated asset inspection gives utilities the insight needed to make data-driven decisions regarding which areas to focus on and prioritize. Rather than relying on manual observations recorded in the database with indicators such as “Yes,” “No,” or “1-2-3-4” against issues like corrosion or structural defects, a digital visual intelligence platform contextualizes all the information from different sources.

Through quick data analysis and training of the AI, this digital solution helps eliminate “human-bottlenecks” in the data evaluation process. Utilities can make better and faster decisions without the need to go back and forth to other files/systems to understand where the asset is located, whether the picture is accurate, and the information is up to date.

Drawing on a detailed view of its entire service territory, the utility can improve inspection scheduling and emergency response prioritizing. This, in turn, improves productivity and lowers the risks to workers and the public from wildfires or major regional outages.

The same asset inspection data can be used to support vegetation management too. GE Vernova’s Visual Intelligence Platform supports encroachment risk analysis and prioritized trim schedules.

GE Vernova’s Visual Intelligence Platform ingests all major visual inspection file types to provide meaningful context. The ability to render 2D or 3D scenes allows the utility to navigate in space and in time to recognize changes and predict asset failures. The AI engine provides the insights needed to help extend the lifespan of aging assets and expedites incident response time.
Visual Intelligence gives utilities the holistic picture needed to identify problems early and detect asset damage or failure to reduce cost and complexity. The platform also provides enhanced public and worker safety and increases compliance.

The GE Vernova platform is the most widely adopted in the industry. We work with the world’s most ambitious utilities and have the most expansive model trained in the industry.