Fluid Leak Detection via Machine Learning and Sensor Fusion
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Solution Overview
Problem
Detecting leaks in fluid containers or conduits, such as swimming pools or plumbing systems, is challenging due to evaporation and the difficulty in distinguishing between evaporation and actual leaks, especially when leaks are slow or minor, making it hard to identify the source and address them promptly.
Innovation Solution
A system comprising sensors that detect vertical displacement data in fluid systems, coupled with a processing system and a platform that uses machine learning to differentiate between normal and abnormal fluid behavior, including leak detection, and can communicate with automated valves or scheduling systems for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fluid level monitoring is used, then evaporation loss cannot be distinguished from leak loss, but implementing advanced sensing and machine learning increases device complexity
Solution Approach 1:
The patent segments the fluid loss problem into two distinct components: evaporation loss and leak loss. By deploying multiple sensors at different locations (e.g., top surface and side walls) and using machine learning to analyze patterns, the system can differentiate between gradual evaporation and sudden leaks, achieving precise measurement without requiring a single complex sensor
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between raw sensor data and leak detection decisions. The ML model processes sensor readings, environmental data, and usage patterns to distinguish evaporation from leaks, effectively mediating the complex analysis task without requiring direct complex hardware intervention at the sensor level
2Reliability
If multiple sensors are deployed to detect slow leaks, then detection reliability improves, but data processing requirements and system complexity increase
Solution Approach 1:
The patent merges data from multiple sensors (fluid level sensors, flow sensors, environmental sensors) into a unified analysis framework. By combining these diverse data streams and processing them through a centralized machine learning model, the system achieves reliable leak detection while managing complexity through integrated processing rather than separate analysis for each sensor
Solution Approach 2:
The patent implements preliminary action by continuously monitoring and baseline-ing normal fluid behavior patterns before leaks occur. The system learns normal evaporation rates and usage patterns in advance, enabling it to quickly identify deviations that indicate leaks without requiring complex real-time analysis when leaks occur
3Loss of substance
If real-time monitoring is implemented to detect leaks early, then water waste is reduced, but energy consumption for continuous monitoring increases
Solution Approach 1:
The patent implements periodic action by using machine learning to determine when continuous monitoring is necessary versus when periodic or event-triggered monitoring suffices. The system monitors continuously during high-risk periods (e.g., nighttime, weekends) and reduces monitoring intensity during low-risk periods, achieving early leak detection while managing energy consumption through adaptive sampling rates
Data Source
AI summary
A system for leak detection in fluid systems, comprising: a device, comprising: a plurality of sensors, each sensor configured to detect data related to a fluid level in the fluid system, and each sensor device deployed in a different fluid system of the plurality of fluid systems, and at least one hardware processor; and software that is configured to, when executed by the at least one hardware processor, receive data from each of the plurality of sensors, and transmit the fluid data to a platform; the platform comprising: an application coupled with a database, and configured to receive the fluid data and train a model to detect events in the fluid system wherein training the model comprises: validate the fluid data, pre-process the data by dividing the fluid data into two parts with different sample rate, converting the fluid data by taking the absolute value of each part, summing them, and then flattening the fluid data to produce flattened data, normalizing the flattened data to remove offsets, calculating a median and subtracting the median from all data points in the flattened data, concatenated the flattened data for each part to form a single dataset, training a model using the single dataset, and deploying the model to the device.