Leak Prediction Algorithm for Pipework
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Solution Overview
Problem
Current leak detection systems are inefficient in predicting and detecting water leaks in pipework, leading to significant water waste and damage, as they often require retrofitted solutions that detect leaks only after they occur, and are not effective in identifying slow leaks until substantial damage is done.
Innovation Solution
A computer-implemented method using supervised training of a leak prediction algorithm with machine learning, specifically an artificial neural network, that receives data from sensors monitoring environmental changes near pipework to predict leaks before they become significant, by classifying patterns and identifying fault scenarios through controlled experiments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional leak detection systems are used, then leaks can be detected after they occur, but significant water waste and damage occur before detection
Solution Approach 1:
The system performs preliminary actions by training machine learning algorithms on historical leak data and environmental sensor data to predict future leaks before they occur. The algorithm continuously monitors environmental conditions (humidity, temperature, pressure) and predicts leak probability, enabling preventive action before water waste occurs.
Solution Approach 2:
The system implements feedback loops where sensor data is continuously fed into the machine learning algorithm, which updates its predictions based on new information. The algorithm learns from actual leak occurrences and adjusts its model, creating a feedback mechanism that improves detection accuracy over time and enables earlier prediction of future leaks.
2Adaptability or versatility
If retrofitted leak detection systems are installed, then existing pipework can be monitored, but the systems are not effective in identifying slow leaks until substantial damage is done
Solution Approach 1:
The patent replaces traditional mechanical leak detection methods (acoustic sensors, flow meters) with a machine learning-based prediction system that uses environmental sensors. Instead of directly measuring leak parameters, the system substitutes a computational model that predicts leak probability based on environmental patterns, achieving higher precision for slow leaks.
Solution Approach 2:
The system changes the parameters being monitored from direct leak parameters (flow rate, acoustic signals) to environmental parameters (humidity, temperature, atmospheric pressure). This parameter transformation allows the machine learning algorithm to detect subtle changes in environmental conditions that precede slow leaks, improving detection precision before substantial damage occurs.
3Measurement precision
If machine learning algorithms are trained on historical data, then prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The training process is segmented into distinct phases: data collection from environmental sensors, data preprocessing and cleaning, model training using historical leak data, validation, and continuous refinement. This segmentation makes the complex training process manageable and systematic, breaking down the overall complexity into smaller, controllable steps.
Solution Approach 2:
The system uses copies of historical leak data and environmental data to train the machine learning algorithm. Instead of requiring real-time complex computations during actual leaks, the system creates a computational model by copying and analyzing past data patterns, simplifying the real-time prediction process while maintaining high accuracy.
Data Source
AI summary
A method of training a leak prediction algorithm to predict leaks from pipework carrying a liquid, comprising: performing supervised training of a computer implemented leak prediction algorithm that receives, as an input, training measurement data from sensors monitoring an environment in proximity to the pipework and provides, as an output, a prediction of whether a leak is likely to occur in future; wherein the supervised training comprises adjusting parameters of the machine learning algorithm to improve the accuracy of the prediction, based on labels indicating which periods of the training measurement data correspond with one or more fault scenarios selected to cause leaks in future.