ML Leak Detection via Multi-Modal Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies struggle to effectively detect fluid leaks in premises, leading to potential property damage from undetected leaks.
Innovation Solution
The use of image, video, and audio analytics, combined with machine learning models, to detect fluid leaks and impending leaks by analyzing data from premises monitoring systems, which can initiate responsive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used, then the system complexity is low, but the leak detection capability is insufficient
Solution Approach 1:
The system divides the monitoring task into multiple specialized components: image analytics module for visual leak detection, video analytics module for temporal pattern recognition, audio analytics module for acoustic signal detection, and machine learning models for intelligent analysis. Each module processes specific types of data independently before integrating results, enabling comprehensive leak detection while managing system complexity through modular architecture.
Solution Approach 2:
The premises monitoring system integrates multiple monitoring functions into a single unified system that can detect fluid leaks, roof leaks, and other property damage events using diverse data sources including images, videos, and audio signals. The machine learning models provide universal pattern recognition across different leak types and environments, enabling one system to perform multiple detection functions simultaneously.
2Measurement precision
If multiple data sources are integrated, then the detection accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The data processing system segments different data types (images, videos, audio) into separate analytics modules that process each format according to its specific characteristics. Image analytics processes visual patterns, video analytics analyzes temporal sequences, and audio analytics detects acoustic signatures. This segmentation allows each module to specialize in optimizing its specific data type while the central coordination layer integrates results, managing processing complexity through functional decomposition.
Solution Approach 2:
The machine learning models serve as intermediary components that receive processed data from various analytics modules and synthesize information to produce unified detection results. These models act as mediators that reconcile different data sources, resolve conflicts, and integrate insights from multiple perspectives into a coherent leak detection determination, thereby managing the complexity of multi-source data integration.
Data Source
AI summary
A system configured to communicate with a plurality of premises monitoring systems. The system includes at least one processor configured to, for each premises monitoring system of a first subset of the premises monitoring systems, receive premises data comprising flood sensor data indicating that a potential water leak, receive user confirmation data confirming that a water leak event occurred at the premises, identify at least one camera associated with the premises monitoring system that captured video of at least a portion of the water leak event, and collect the video captured by the at least one camera. A machine learning (ML) model is trained using the video collected from each premises monitoring system of the first subset of the premises monitoring systems. The ML model is deployed to at least a second subset of the plurality of premises monitoring systems for detecting a water leak.