Remote Visual Inspection Using Adaptive Image Failure Detection
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Solution Overview
Problem
Existing remote monitoring systems for oilfield facilities rely heavily on human inspection, which is time-intensive, costly, and prone to human error, failing to accurately detect contamination and equipment failures due to the high volume of data requiring meticulous attention.
Innovation Solution
A method and system utilizing digital cameras connected to a network, with an adaptive object-detection function on a remote server to identify and monitor specific interest regions for potential failures, reducing human intervention by automatically analyzing image data and adapting to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators conduct manual inspection of facilities, then detection accuracy can be maintained through meticulous attention to detail, but inspection time and cost increase significantly
Solution Approach 1:
The system enables self-service monitoring where the automated image analysis system performs inspection tasks independently without requiring human operators to manually examine each image, thus reducing inspection time while maintaining detection accuracy through algorithmic analysis
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated computer-based image analysis system that uses algorithms to detect anomalies, eliminating the time-consuming manual review process while maintaining or improving detection accuracy
2Object-affected harmful factors
If human operators review captured images and sensor data remotely, then safety risks are reduced by minimizing on-site visits, but human error increases due to the high volume of data requiring attention
Solution Approach 1:
The system replaces human operators with an automated image analysis system that processes captured images and sensor data, eliminating human error while maintaining the safety benefit of remote monitoring. The automated system consistently applies detection algorithms without fatigue or distraction
Solution Approach 2:
The patent introduces an automated image analysis system as an intermediary between the captured data and the final detection results, filtering and analyzing data through algorithmic processes that reduce human error while maintaining remote monitoring safety advantages
3Ease of operation
If remote cameras are installed to capture images of facilities, then the number of on-site visits is reduced, enhancing safety and reducing cost, but detection accuracy decreases due to the inability to perform meticulous visual inspection
Solution Approach 1:
The system replaces manual visual inspection with automated computer-based image analysis that processes remote camera captures, maintaining the operational convenience of remote monitoring while improving detection accuracy through algorithmic analysis capabilities that can identify subtle anomalies
Solution Approach 2:
The patent applies image processing techniques that enhance image parameters such as contrast, brightness, and feature detection sensitivity, allowing the remote camera system to achieve detection accuracy comparable to or exceeding manual inspection by optimizing the digital image characteristics
4Adaptability or versatility
If multiple sensors and cameras are deployed to monitor various parameters, then comprehensive monitoring coverage is achieved, but data volume increases making human review more error-prone
Solution Approach 1:
The automated image analysis system serves as an intermediary that processes and integrates data from multiple sensors and cameras, managing the large data volume through systematic algorithmic analysis that prevents the errors associated with human review of extensive multi-parameter data sets
Solution Approach 2:
The system segments and prioritizes analysis of different data sources and image regions, focusing computational resources on areas most likely to contain anomalies, thus efficiently handling comprehensive monitoring data without overwhelming the analysis process
Data Source
AI summary
A method and system for remote visual inspection and failure detection at industrial sites, utilizing pre-existing digital cameras connected to a server. The server hosts adaptive object-detection software that analyzes images from the cameras to identify focus cues, which indicate areas prone to failure. The system contextualizes these images to define interest regions that require ongoing monitoring. In a continuous monitoring loop, new images are periodically received, processed, and analyzed for visible failure attributes. Detected potential failures trigger notifications to users via a human interface, allowing them to validate or modify the system's findings. User feedback is used to enhance the software's accuracy over time. The system supports various camera types and is adaptable to multiple monitoring environments, offering a robust solution for automating failure detection without requiring specialized site hardware.