Water Leak Detection via Flow Rate Pattern Matching
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Solution Overview
Problem
Current fluid supply monitoring systems lack the ability to accurately detect leaks and classify water consumption among various implements, leading to inefficiencies and potential property damage due to false alarms and inadequate water usage analysis.
Innovation Solution
A monitoring system equipped with a fluid sensor and controller that compares flow rate data to preconfigured models to classify water consumption and identify leaks, using machine learning to optimize consumption models and distinguish between different consumption implements, thereby reducing false alarms and improving water management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow rate data is compared to preconfigured models to classify water consumption, then water consumption classification accuracy is improved, but device complexity increases
Solution Approach 1:
The system preconfigures multiple flow rate models representing different water implements (shower, bathtub, sink, toilet) before operation. These models are stored in memory and used for comparison during runtime, eliminating the need for real-time complex analysis and enabling accurate classification through pattern matching.
Solution Approach 2:
The system creates digital copies of flow rate characteristics for various water implements by capturing actual flow data and generating corresponding models. These copied models are then used for comparison and classification, allowing the system to identify implements based on their flow rate signatures without requiring direct physical measurement of each implement.
2Measurement precision
If machine learning is used to optimize consumption models, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The machine learning optimization operates periodically rather than continuously. The system collects flow rate data over time and updates the preconfigured models at scheduled intervals or when sufficient data is accumulated, reducing continuous processing energy requirements while maintaining model accuracy through periodic refinement.
3Reliability
If flow rate data is monitored and compared continuously, then leak detection reliability is improved, but loss of time for data processing increases
Solution Approach 1:
The system replaces complex continuous mathematical analysis with a simpler pattern recognition approach. By comparing measured flow rate data against preconfigured models using threshold-based and pattern-matching algorithms, the system achieves reliable leak detection without requiring intensive continuous computation, thus reducing processing time.
Data Source
AI summary
A water supply monitoring system in connection with a supply line includes a fluid sensor configured to identify flow rate data identifying a flow rate of water through the supply line. A controller is configured to receive the flow rate data from the fluid sensor and compare the flow rate data over time to a plurality of flow rate models. The flow rate models define flow rate characteristics of a plurality of preconfigured water implements. The controller is further configured to classify a water consumption identified by the flow rate data as being consumed by a plurality of consumption implements in connection with the supply line in a plurality of consumption classifications.