Liquid Leak Detection Using Global Sequence Alignment
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Solution Overview
Problem
Conventional methods for detecting water leaks in distribution systems are inadequate due to interference from external factors, high sampling rates required for accurate detection, and inefficiencies in handling compound fixtures, leading to false positives and high processing power consumption, making them unsuitable for real-time monitoring in residential settings.
Innovation Solution
A system utilizing global sequence alignment techniques to compare periodic water pressure samples from a representative node in a liquid distribution system with historical data, allowing for reliable leak detection with reduced sampling frequency and processing power, enabling wireless notifications through cloud computing and IoT connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rates (e.g., 1 KHz) are used to detect short-duration fixture usage events, then measurement precision is improved, but use of energy increases significantly
Solution Approach 1:
The system dynamically adjusts the sampling rate based on the detected water usage pattern duration. For short-duration events like hand washing, a higher sampling rate is temporarily applied, while for longer events like showers, a lower sampling rate suffices. This dynamic adaptation resolves the contradiction by matching resource consumption to actual detection needs rather than using a fixed high sampling rate continuously.
Solution Approach 2:
The patent changes the sampling rate parameter based on the specific detection scenario and fixture type. Instead of maintaining a constant high sampling rate, the system adjusts this parameter dynamically according to the expected duration and characteristics of water usage events, thereby reducing overall power consumption while maintaining detection precision when needed.
2Stability of the object's composition
If pressure regulators are used to maintain set point pressure, then stability of water pressure is improved, but ability to detect pressure drops from leaks is worsened
Solution Approach 1:
The system segments the pressure monitoring function from the pressure regulation function. The pressure regulator maintains overall system stability, while a separate monitoring system independently tracks pressure variations over time. This segmentation allows the regulator to perform its stabilizing function while the monitoring system detects anomalies that indicate leaks, resolving the contradiction between stability and detection sensitivity.
Solution Approach 2:
The system implements feedback by continuously comparing current pressure readings against historical baseline data and expected pressure profiles for different water usage events. When pressure deviations fall outside expected ranges, the system generates leak alerts. This feedback mechanism enables accurate leak detection despite the presence of pressure regulators, as the system learns normal pressure variations and identifies true anomalies.
3Adaptability or versatility
If signature classification is applied to compound fixtures with multiple components, then detection coverage is improved, but device complexity increases due to independent consumption characteristics
Solution Approach 1:
The system merges the signature classification approach with machine learning techniques that can handle compound fixtures. Instead of requiring separate signatures for each component of a compound fixture, the system learns aggregate usage patterns that represent the combined behavior of multiple components. This merging resolves the contradiction by maintaining detection coverage for complex fixtures while avoiding the exponential increase in signature modeling complexity.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw pressure data and leak detection decisions. These models automatically learn and adapt to the usage patterns of compound fixtures with multiple components, translating complex independent consumption characteristics into unified detection rules. This intermediary layer simplifies the overall system while maintaining versatility in detecting leaks across diverse fixture types.
Data Source
AI summary
Systems and methods relate to monitoring liquid pressure to detect liquid leaks based on a global sequence alignment technique. A monitoring device in a liquid distribution obtains periodic samples at regular time intervals from a representative node of a liquid distribution system. Sequences of the samples are formed for a current time instance and for historical data comprising samples at the same node related to the same time instance, such as samples which are a day old and/or a week old. Global sequence alignment algorithms are implemented at a cloud computing service to determine if there are deviations from liquid pressure patterns at the node which are indicative of a liquid leak. Related notifications are provided wirelessly to user devices.