Pipe Leak Detection Using Telematics and Irregular Usage Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Homeowners often fail to detect leaks in pipes due to infrequent checks, leading to unnoticed damage, especially in less frequently used buildings, resulting in extensive damage.
Innovation Solution
A system utilizing home telematics data and machine learning algorithms to detect irregular pipe activity, alert users to leaks, and automatically take corrective actions such as shutting off water valves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homeowners manually check pipes frequently, then leak detection reliability improves, but ease of operation deteriorates due to the burden of frequent manual inspections
Solution Approach 1:
The system enables automatic leak detection through sensors that continuously monitor pipe conditions without requiring homeowner intervention. The machine learning algorithm automatically analyzes sensor data to detect leaks, freeing homeowners from manual inspection tasks while maintaining high detection reliability.
Solution Approach 2:
The patent replaces manual visual inspection with automated sensor-based detection systems. Sensors monitor acoustic emissions, vibration, and other physical indicators of pipe leaks, substituting human sensory and manual examination with automated electronic detection mechanisms.
2Measurement precision
If additional sensors and monitoring devices are installed, then leak detection precision improves, but device complexity increases
Solution Approach 1:
The system uses multi-functional sensors that can detect multiple types of pipe issues (leaks, freezing, abnormal flow) simultaneously. A single sensor node can monitor acoustic emissions, vibration, temperature, and flow characteristics, reducing the need for multiple specialized devices while maintaining comprehensive detection capability.
Solution Approach 2:
The patent combines multiple detection functions into an integrated system where sensors, machine learning algorithms, and alert mechanisms work together as a unified platform. The machine learning model processes multiple sensor inputs simultaneously to detect leaks, merging data processing and decision-making functions into a single computational system.
3Reliability
If automated shutdown systems are implemented, then damage prevention effectiveness improves, but loss of time increases due to system response delays
Solution Approach 1:
The system pre-positions shutdown mechanisms (electromagnetic valves, actuators) adjacent to pipe sections, allowing immediate isolation of affected areas upon leak detection. The machine learning algorithm is pre-trained with leak detection patterns, enabling rapid identification and response without deliberation delays.
Solution Approach 2:
The system implements real-time feedback loops where sensors continuously monitor pipe conditions and immediately feed data to the machine learning algorithm, which instantly determines whether shutdown action is required. This closed-loop feedback system minimizes response time by eliminating manual assessment delays.
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
Efficiently detects pipe leaks and prevents damage by providing timely alerts and automated responses, reducing the risk of extensive water damage.
Implementation Method 1
The one or more sensors may include at least one of: (i) vibration sensors
Implementation Method 2
The one or more sensors may include at least one of: (i) vibration sensors; (ii) audio sensors
Data Source
AI summary
Systems and methods are described for detecting a leak based upon home telematics data. The method may include: (1) receiving home telematics data from one or more sensors associated with one or more pipes (or piping systems) in a structure, wherein the home telematics data is indicative of the frequency with which the one or more pipes are being used; (2) determining, using a trained machine learning algorithm, pipe activity associated with the one or more pipes is occurring at an irregular frequency; (3) determining, based upon at least the determination that the pipe activity associated with the one or more pipes is occurring at the irregular frequency, that the one or more pipes are leaking; and (4) transmitting an indication to a user associated with the home that the one or more pipes are leaking.