Underground Pipe Acoustic Leak Detection With Distributed ML Sensors
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Solution Overview
Problem
Current manual methods for detecting underground pipe leaks are inefficient, lack stability and coverage, and rely heavily on individual inspector experience, leading to significant water loss due to undetected leaks in aging infrastructure.
Innovation Solution
An underground pipe leak detection system utilizing IoT, signal processing, and machine learning to analyze acoustic signals from underground pipes, employing classification and regression models to determine leak presence, shape, size, material, and location using a network of sensors and processing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual detection methods are used by inspectors, then device complexity is reduced, but detection coverage and reliability deteriorate
Solution Approach 1:
The detection system is segmented into multiple independent sensing devices distributed along the pipe network, each capable of autonomous acoustic monitoring. This segmentation enables comprehensive coverage without requiring a single complex centralized system, resolving the contradiction between simplicity and reliability.
Solution Approach 2:
Manual mechanical inspection methods are replaced with automated acoustic sensing devices that use signal processing and machine learning algorithms. This substitution maintains operational simplicity while dramatically improving detection reliability through objective, data-driven leak identification.
2Reliability
If widespread sensors are deployed to expand detection coverage, then detection coverage improves, but device complexity increases
Solution Approach 1:
Each sensing device is designed as a universal, multi-functional unit that combines acoustic sensing, signal processing, and leak detection capabilities. This universality allows widespread deployment of identical standardized devices, expanding coverage without proportionally increasing system complexity.
Solution Approach 2:
The sensing devices operate autonomously, performing self-calibration, self-diagnosis, and automatic leak detection without requiring complex centralized control. This self-service capability enables distributed deployment while maintaining system simplicity through autonomous operation at each node.
3Ease of operation
If manual detection based on inspector experience is used, then ease of operation is maintained, but productivity and detection speed deteriorate
Solution Approach 1:
Human inspector experience and manual detection operations are replaced with automated acoustic analysis systems using machine learning models. This substitution maintains ease of operation through simple device deployment while dramatically improving productivity through continuous, high-speed automated monitoring.
Solution Approach 2:
Machine learning models serve as intermediaries between raw acoustic signals and leak detection decisions, automatically processing and interpreting acoustic data. This intermediary layer eliminates the need for human expertise in operation while maintaining simple deployment, thereby improving productivity without sacrificing ease of use.
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 system provides immediate, efficient, and widespread detection of leaks, reducing manpower requirements and increasing detection coverage, enabling precise localization of leaks.
Implementation Method 1
a sensing device (101)... for collecting a voice signal from an underground pipe during a time period
Implementation Method 2
The processing device is further configured to perform a Fast Fourier Transform on the voice signal to extract the time domain features and the frequency domain features of the voice signal
Data Source
AI summary
Provided is an underground pipe leak detection system, including a sensing device, a storage device, and a processing device. The sensing device is used for collecting a voice signal from an underground pipe during a time period. The storage device is used for storing a voice dataset, and storing the voice signal transmitted by the sensing device. The processing device may access the storage device. The processing device is configured to execute the following operations: training a classification model using the voice dataset; extracting features of the voice signal; inputting the features of the voice signal into the classification model that has been trained to determine if there is a leak in the underground pipe.