AI-Optimized Utility Pipe Sensor Placement
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Solution Overview
Problem
Current methods for detecting hidden water leaks in utility pipes are inefficient and costly, as they rely on subjective human expert opinions for sensor placement, leading to suboptimal detection of leaks and significant losses in revenue and environmental impact.
Innovation Solution
A computer-implemented system using machine learning techniques to calculate the likelihood of failure and consequence of failure for utility pipes, determining optimal sensor placement based on a risk metric that combines likelihood and consequence scores, enabling strategic deployment of sensors to monitor high-risk areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic sensors are placed in water hydrants to detect leaks, then leak detection accuracy is improved, but deployment cost and complexity increase significantly
Solution Approach 1:
The system uses existing water meter infrastructure to perform leak detection functions. Water meters already deployed in the network are equipped with sensors and processing capabilities to detect leaks locally, eliminating the need for separate acoustic sensor deployments in hydrants. The existing infrastructure serves itself by adding leak detection functionality to water meters that are already present in the system.
Solution Approach 2:
Water meters are designed to perform multiple functions: traditional water measurement and billing, plus leak detection and localization. By making the water meter a multi-functional device that handles both metering and leak detection, the system avoids deploying dedicated leak detection sensors throughout the network, thereby reducing overall device complexity while maintaining detection accuracy.
2Reliability
If sensors are deployed across the complete water pipe infrastructure, then hidden leak detection coverage is improved, but deployment cost becomes prohibitive
Solution Approach 1:
The system leverages the existing water meter infrastructure that is already universally deployed across the water network. Instead of adding new sensors throughout the system, it enables the existing water meters to perform leak detection, thus achieving network-wide coverage without proportional increases in deployment complexity or cost.
Solution Approach 2:
Rather than requiring sensors at every possible location, the system uses the subset of locations where water meters are already installed. This partial deployment approach achieves sufficient leak detection coverage by utilizing the existing meter infrastructure, avoiding the excessive action of deploying sensors everywhere in the network.
3Productivity
If sensor placement is determined by human expert opinion, then deployment speed is maintained, but optimization of sensor locations deteriorates
Solution Approach 1:
The system replaces the mechanical process of human expert assessment with an automated computational algorithm. The optimization algorithm automatically analyzes the water network topology, flow patterns, and historical leak data to determine optimal sensor placements, substituting human judgment with algorithmic optimization while maintaining fast deployment through automated processing.
Solution Approach 2:
The system incorporates feedback mechanisms where leak detection results and sensor performance data are continuously analyzed to refine and improve sensor placement optimization. The algorithm learns from actual leak detection outcomes and adjusts future sensor placement recommendations, creating a feedback loop that continuously improves placement optimization while maintaining deployment efficiency.
Data Source
AI summary
A computer-implemented method and system for determining placement of a sensor component on a utility pipe. Data relating to the utility pipe is inputted which is processed to generate one or more variables. One or more models are trained, via the one or more variables, to produce an output indicative of a likelihood of failure variable associated with the utility pipe from each model. The outputs from all models are preferably combined into an ensemble output indicative of a likelihood of failure associated with the utility pipe. A consequence of failure variable associated with the utility pipe is determined preferably utilizing a plurality of weighted variables. A sensor placement determinative variable is then determined contingent upon the ensemble output and the consequence of failure variable associated with the utility pipe. Feedback data is then provided indicative of physical placement of one or more sensor components associated with the utility pipe based at least in part on the sensor placement determinative variable.