Statistical Learning Model for Fluid Network Leak Characterization
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Solution Overview
Problem
Current methods for detecting and characterizing leaks in fluid networks, such as vibro-acoustic listening and sectorization, are ineffective in precisely locating and determining the severity of leaks, especially in complex networks, leading to inefficient maintenance and high resource utilization.
Innovation Solution
A method involving the training of a statistical learning model using vibro-acoustic sensors to characterize leaks by associating leak data with vibro-acoustic signals, incorporating digital mapping, simulated sensors, and sectorization data to determine leak type and flow rate, allowing for remote and accurate leak characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vibro-acoustic listening methods are used to detect leaks, then leak detection effectiveness is improved, but the number of sensors required increases and acoustic disturbances from the environment affect measurement accuracy
Solution Approach 1:
The network is divided into sectors with flow meters at inlet and outlet points, allowing localized leak detection without requiring dense sensor coverage throughout the entire network. This segmentation enables effective leak detection while reducing the total number of sensors needed.
Solution Approach 2:
Flow meters serve as intermediaries to detect leaks indirectly by measuring flow rate differences between inlet and outlet of sectors, rather than directly measuring acoustic signals from leaks. This approach avoids acoustic disturbances from the environment while maintaining detection effectiveness.
2Device complexity
If sectorization methods are used to detect leaks, then the need for numerous sensors is reduced, but precise leak location and severity characterization are not achieved
Solution Approach 1:
The method combines sectorization approach with vibro-acoustic sensing by integrating flow rate data from sector boundaries with acoustic signal analysis at strategic points. This merging enables both reduced sensor density and improved leak characterization capability.
Solution Approach 2:
The method adds the dimension of acoustic signal analysis to the traditional flow-based sectorization approach. By incorporating vibro-acoustic data alongside flow rate measurements, the system achieves both leak detection and severity characterization without requiring dense sensor coverage.
3Measurement precision
If acoustic listening methods are used to locate leaks, then leak location capability is improved, but leak severity characterization is not provided
Solution Approach 1:
The vibro-acoustic sensors serve multiple functions: they detect the presence of leaks, locate their positions, and characterize their severity through acoustic signal analysis. This multi-functionality eliminates the need for separate systems for each purpose and provides comprehensive leak information.
Solution Approach 2:
The method creates a virtual representation of the network state by combining flow rate data from sector boundaries with acoustic signal characteristics. This virtual model enables simultaneous determination of leak location and severity without requiring direct measurement at every point in the network.
4Adaptability or versatility
If multiple leaks of various types and severities are present in the network, then comprehensive leak detection is required, but current methods cannot discriminate between leak severities for prioritized maintenance
Solution Approach 1:
The method applies different analysis approaches to different sectors and locations based on their specific characteristics. By tailoring the vibro-acoustic analysis to local conditions and network configurations, the system can accurately characterize leak severity in each location, enabling prioritized maintenance decisions.
Solution Approach 2:
The system analyzes changes in acoustic signal parameters (such as frequency, amplitude, and temporal characteristics) to differentiate between leak types and severities. By monitoring parameter variations over time and across different locations, the method achieves precise leak characterization for prioritized maintenance.
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
Enables precise characterization of leaks, optimizing maintenance by prioritizing repairs and reducing resource consumption, while enhancing the model's accuracy through simulated and real sensor data integration.
Implementation Method 1
The vibro-acoustic listening methods aim at locally listening, using a microphone for example, to the signals emitted by the leaks in the pipes
Implementation Method 2
listening, using a microphone for example, to the signals emitted by the leaks in the pipes
Data Source
AI summary
A method for characterizing a leak in a fluid network, making it possible to determine the type and/or the flow rate of a leak in a fluid network, in which the fluid network is equipped with a plurality of vibro-acoustic sensors configured to provide vibro-acoustic signals, and in which a statistical learning model receives as input at least one vibro-acoustic signal obtained directly or indirectly from at least one vibro-acoustic sensor and provides as output at least one leak characterization data among the leak type and the leak flow rate.