Adaptive Fluid Consumption Profiles for Anomaly Detection
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Solution Overview
Problem
Existing systems struggle to effectively detect anomalies in the consumption of fluids, such as leaks or improperly closed valves, in consuming systems like houses or factories, without considering the evolution of consumption patterns over time.
Innovation Solution
A method involving a measuring device to monitor fluid consumption, a processing unit to determine consumption profiles, and a database to establish an adaptive anomaly threshold based on historical consumption data, allowing for real-time detection of anomalies by comparing current profiles against the threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed anomaly threshold is used for detecting fluid consumption anomalies, then the detection method is simple to implement, but it cannot adapt to changes in consumption patterns over time, leading to reduced detection accuracy
Solution Approach 1:
The anomaly threshold is transformed from a static fixed value to a dynamic adaptive threshold that automatically adjusts based on historical consumption data. The processing unit continuously updates the threshold by comparing current consumption profiles with historical profiles, allowing the system to adapt to seasonal variations, changing usage patterns, and evolving consumption behaviors without manual intervention.
Solution Approach 2:
The system implements a feedback mechanism where detection results and consumption data are continuously fed back into the processing unit. This feedback loop allows the system to learn from past anomalies and normal consumption patterns, refining the anomaly threshold over time. The processing unit uses this feedback to adjust the threshold dynamically, improving detection accuracy while maintaining automated operation.
2Measurement precision
If historical consumption data is stored and processed to establish adaptive thresholds, then detection accuracy improves, but the quantity of data to be processed and stored increases
Solution Approach 1:
The system extracts only the essential features from historical consumption data that are relevant for anomaly detection. Instead of storing and processing all raw consumption data, the processing unit identifies and retains key characteristics such as typical consumption patterns, seasonal variations, and baseline profiles. This extraction approach maintains high detection accuracy while significantly reducing the volume of data that needs to be stored and processed.
Solution Approach 2:
The system transforms raw consumption data into processed consumption profiles that capture the essential characteristics of fluid usage patterns. By changing the parameters from raw volumetric data to normalized profiles representing typical consumption behaviors, the system reduces data complexity and storage requirements while preserving the information necessary for accurate anomaly detection.
3Reliability
If the anomaly threshold is updated continuously with new data, then the system remains adaptive to current patterns, but the processing time and computational load increase
Solution Approach 1:
The system implements periodic updates of the anomaly threshold rather than continuous real-time updates. The processing unit recalibrates the threshold at predetermined intervals or when significant changes in consumption patterns are detected. This periodic approach maintains the threshold's relevance to current patterns while avoiding the excessive computational load of continuous updates, thereby reducing processing time and computational requirements.
Data Source
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AI summary
The invention relates to a method for detecting an anomaly in consumption of a fluid by a consuming system (1), comprising the following steps: o providing a database of anomaly-free-consumption-profile data; o determining an anomaly threshold; o determining a current consumption profile of the consuming system, from a measurement signal; o comparing the current consumption profile to the anomaly threshold, and: • if the current consumption profile has a value below the anomaly threshold: identifying an absence of consumption anomaly, and adding the current consumption profile to the database; • if the current consumption profile has a value above the anomaly threshold: identifying the presence of a consumption anomaly, and not adding the current consumption profile to the database.