Autonomous UAV Inspection of Gas Sensors in Hazardous Facilities
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Solution Overview
Problem
Current inspection methods for processing facilities, such as oil and gas facilities, are inefficient, costly, and pose health risks to human operators due to exposure to hazardous emissions, with inconsistent results and limited detection capabilities.
Innovation Solution
Utilizing unmanned autonomous vehicles to autonomously navigate and inspect gas sensors, perform calibration, and learn from previous inspections, optimizing data capture based on real-time conditions and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators perform inspections using handheld devices, then inspection results can be obtained, but health risks arise due to exposure to hazardous emissions and inspection efficiency is low
Solution Approach 1:
The unmanned autonomous vehicle performs inspections autonomously without human intervention. The vehicle navigates independently through the facility, detects anomalies using onboard sensors, and returns data for analysis, enabling the inspection system to serve itself without requiring human operators to enter hazardous areas.
Solution Approach 2:
The patent replaces the mechanical system of human operators physically inspecting facilities with an unmanned autonomous vehicle equipped with sensors, cameras, and navigation systems. This substitution eliminates human exposure to hazardous environments while maintaining inspection capabilities through automated detection and data collection systems.
2Measurement precision
If traditional inspection methods are used, then inspection coverage is achieved, but detection capability is limited and results are inconsistent
Solution Approach 1:
The unmanned autonomous vehicle is designed with multi-functional capabilities including various sensors (gas detectors, cameras, LIDAR), navigation systems, and communication equipment. This universal platform can perform multiple inspection tasks across different facility types and detect various anomalies, significantly enhancing detection capability and adaptability compared to specialized handheld devices.
Solution Approach 2:
The vehicle incorporates real-time data collection and analysis systems that provide feedback on environmental conditions, sensor performance, and detected anomalies. This feedback mechanism enables continuous optimization of detection algorithms, improves measurement precision through adaptive threshold adjustment, and ensures consistent results across different inspection scenarios.
Data Source
AI summary
The disclosed techniques are directed to using unmanned autonomous vehicles to perform inspections of, for example, gas sensors or other assets located within a processing facility. For example, the unmanned autonomous vehicles may autonomously navigate through a processing facility to perform the inspections. In addition, one or more properties of data captured by the unmanned autonomous vehicles may be controlled based on real-time conditions to optimize the inspection of the assets of the processing facility. Furthermore, the unmanned autonomous vehicles may be configured to perform calibration of the assets when anomalies readings are collected. In addition, the unmanned autonomous vehicles may be self-learning autonomous devices configured to learning from data collected during previous inspections of assets.