Fluid Network Sensor Placement for Real-Time Anomaly Detection
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Solution Overview
Problem
Fluid networks, such as water and gas distribution systems, often experience issues like leaks, corrosion, and contamination that are difficult to detect in a timely manner, leading to significant impacts on infrastructure and health.
Innovation Solution
A method and system for optimizing sensor placement in fluid networks using a model represented as a matrix data structure, allowing for real-time data collection and analysis to detect anomalies and predict potential problems, involving sensors positioned at strategic locations and a processor outside the network for data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are deployed throughout the fluid network, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The system performs preliminary actions by creating a digital model of the fluid network and using algorithms to pre-determine optimal sensor placement locations. This preliminary analysis identifies the minimum set of sensor locations needed to achieve complete network coverage, avoiding the need to deploy sensors throughout the entire network while maintaining full detection capability.
2Measurement precision
If more sensors are placed in the network, then detection coverage is improved, but loss of time for data processing increases
Solution Approach 1:
The system extracts and processes only the essential data from optimally positioned sensors rather than processing data from numerous sensors throughout the network. The digital model and algorithmic analysis extract the minimum necessary sensor locations and data points needed for complete network monitoring, significantly reducing data processing time while maintaining full detection coverage.
3Measurement precision
If sensors are placed to cover all areas, then detection capability is improved, but the system becomes less adaptable to changing network conditions
Solution Approach 1:
The system implements dynamic adaptability by allowing the digital model and sensor placement optimization to be recalculated when network conditions change. The algorithm can dynamically adjust sensor placement recommendations based on changing network topology, flow patterns, or detected anomalies, making the system adaptable rather than static despite using a structured modeling approach.
Data Source
AI summary
Systems and methods are provided for optimally determining sensor or infrastructure placement in a fluid network, for determining an anomaly of interest in the fluid network, and for determining sensor coverage in a fluid network, which are based on a model of the fluid network represented by a directed graph.