Multivariate Sensing With Local and Cloud ML for Gas Leak Detection
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Solution Overview
Problem
Existing gas leak detection technologies are costly, impractical for remote installations, require frequent maintenance, and lack precision in pinpointing leak locations, especially in industrial systems.
Innovation Solution
A multivariate sensing apparatus using machine learning to analyze temperature, vibration, and sound patterns to indirectly detect gas leaks, reducing maintenance needs and operating costs, and enabling precise leak detection across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If direct measurement sensors (metal oxide sensors, lidar, special cameras) are used to detect gas leaks, then detection capability is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses sound waves as an intermediary medium to detect gas leaks indirectly. Instead of directly measuring gas composition with complex sensors, the system captures acoustic signals from leaking gas and analyzes their patterns to identify leak conditions, thereby simplifying the sensor hardware while maintaining detection capability
Solution Approach 2:
The patent replaces complex mechanical and optical sensing systems (metal oxide sensors, lidar, special cameras) with a simpler acoustic measurement system. By substituting the detection mechanism from direct physical/chemical sensing to acoustic field measurement, the system achieves gas leak detection with significantly reduced device complexity
2Measurement precision
If specialized sensors with dedicated power connections are used, then detection precision is improved, but ease of installation and maintenance deteriorates
Solution Approach 1:
The patent employs universal sensors that can function with standard power sources rather than requiring dedicated power connections. The acoustic sensors and processing system are designed to operate with common power supplies, enabling deployment in diverse locations including remote areas without specialized infrastructure, thereby improving installation ease while maintaining detection precision
Solution Approach 2:
The system incorporates self-contained sensor units that can be independently installed and operated without requiring complex power infrastructure. The sensors are designed to be self-sufficient with standard power sources, eliminating the need for dedicated power connections and specialized installation teams, thus improving maintenance ease
3Ease of operation
If battery-powered sensors are used for remote installations, then ease of installation is improved, but power consumption increases leading to frequent battery changes
Solution Approach 1:
The patent implements periodic sampling and transmission of acoustic data rather than continuous monitoring and transmission. The system performs measurements at intervals and only transmits data when anomalies are detected or at scheduled times, significantly reducing power consumption while maintaining effective leak detection capability for remote installations
4Area of stationary object
If fenceline monitoring sensors are deployed around plant fences, then coverage area is improved, but measurement precision for pinpointing leak location deteriorates
Solution Approach 1:
The patent transitions from two-dimensional fenceline monitoring to three-dimensional spatial analysis by deploying acoustic sensors at multiple heights and positions. The system captures acoustic signals from various dimensions and uses pattern recognition to triangulate leak locations with precision, enabling both wide coverage and accurate location identification
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
The solution provides accurate, cost-effective, and low-maintenance gas leak detection, suitable for remote installations, with improved precision in identifying leak sources.
Implementation Method 1
analyze temperature, vibration, and sound patterns
Implementation Method 2
analyze temperature, vibration, and sound patterns
Implementation Method 3
analyze temperature, vibration, and sound patterns
Data Source
AI summary
A multivariate sensing device is to be coupled to a containment asset designed to contain a pressurized fluid, the multivariate sensing device comprising: sensors to provide sensor data, circuitry to implement a local ML engine operable in accordance with a first ML model to detect leaks of the pressurized fluid from the containment asset based on the sensor data; a wireless interface to couple the multivariate sensing device to a cloud ML engine operable in accordance with a second ML model to detect leaks of the pressurize fluid with greater accuracy, the multivariate sensing device to transmit a first message via the wireless interface to the cloud-based management service in response to a detected leak, the cloud ML engine to determine if the sensor data indicates a leak and, if not, then the cloud-based management service is to transmit a message to the multivariate sensing device for additional training.