Mobile Gas Leak Detection Using Bayesian 2D Probability Mapping
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Solution Overview
Problem
Current methods for detecting natural gas leaks in infrastructure are slow, costly, and prone to human bias, necessitating improved systems for quick and efficient detection with high accuracy.
Innovation Solution
The use of a mobile measurement device equipped with a gas analyzer and wind measurement tools, combined with Bayesian updating of 2-D surface maps, to generate and update probability distributions of gas emission source locations based on multiple measurement runs, reducing human error and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a moving vehicle is used to cover more ground for gas leak detection, then the survey area increases, but the accuracy of locating the gas leak source decreases
Solution Approach 1:
The patent transitions from traditional 1D linear sampling along vehicle paths to 2D surface mapping of gas emission source probabilities. By collecting gas concentration data at multiple locations and times and processing it through Bayesian updating algorithms, the system creates a comprehensive 2D probability surface that identifies likely leak locations across the entire survey area, resolving the contradiction between covering large areas and maintaining localization accuracy.
2Measurement precision
If traditional survey methods are used, then detection accuracy may be high, but the time and cost required for detection increase
Solution Approach 1:
The patent replaces traditional mechanical survey methods with a computational system that uses mobile measurement devices to collect data and Bayesian algorithms to process it. This substitution of mechanical inspection with computational analysis enables rapid processing of large datasets from multiple measurement runs, maintaining high detection accuracy while significantly reducing the time required for comprehensive survey coverage.
3Reliability
If multiple measurement runs are performed to improve detection reliability, then detection accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent merges data from multiple measurement runs into a unified 2D probability surface through Bayesian updating. Instead of treating each measurement run separately, the system combines all collected gas concentration data, atmospheric conditions, and measurement locations into a single comprehensive analysis, thereby improving detection reliability through multiple runs while maintaining efficiency through integrated processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid and accurate localization of gas leaks, reducing the time and cost associated with detection while minimizing human bias, thereby enhancing public safety and resource allocation.
Implementation Method 1
A natural gas detector apparatus is mounted to the vehicle so that the vehicle transports the detector apparatus over an area of interest at speeds of up to 20 miles per hour. The apparatus is arranged such that natural gas intercepts a beam path and absorbs representative wavelengths of a light beam. A receiver section receives a portion of the light beam onto an electro-optical etalon for detecting the gas.
Data Source
AI summary
In some embodiments, vehicle-based natural gas leak detection methods are used to generate 2-D spatial distributions (heat maps) of gas emission source probabilities and surveyed area locations using measured gas concentrations and associated geospatial (e.g. GPS) locations, wind direction and wind speed, and atmospheric condition data. Bayesian updates are used to incorporate the results of one or more measurement runs into computed spatial distributions. Operating in gas-emission plume space rather than raw concentration data space allows reducing the computational complexity of updating gas emission source probability heat maps. Gas pipeline location data and other external data may be used to determine the heat map data.