Fuzzy Logic Temporal Fault Reasoning for HVAC Systems
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Solution Overview
Problem
In dynamic systems like HVAC, faults often remain unobserved until they cause damage, making early detection challenging due to indirect observability and ambiguity in fault diagnosis, where one fault can be observed in multiple states and each state can be explained by multiple faults.
Innovation Solution
The approach transforms sensor readings into fuzzy observable states, using a mapping table to admit or exclude faults, and updates fault scores over time based on admitting and excluding rates, allowing for online processing without historical data access, capturing expert knowledge with transparent semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault detection methods are used, then the system structure remains simple, but faults remain unobserved until they cause damage
Solution Approach 1:
The patent introduces a fuzzy logic reasoning system as an intermediary between sensor observations and fault diagnosis. This intermediary processes ambiguous sensor data through fuzzy sets and temporal aggregation to produce reliable fault detection, resolving the contradiction by adding intelligence rather than physical complexity to the system.
Solution Approach 2:
The patent transforms crisp sensor readings into fuzzy membership values, changing the parameter representation from binary/precise to continuous/ambiguous. This parameter transformation enables the system to handle uncertain fault indicators and achieve reliable detection without requiring complex hardware modifications.
2Loss of time
If direct fault observation is attempted, then detection speed increases, but faults cannot be directly observed and remain hidden
Solution Approach 1:
The patent performs preliminary aggregation of fault indicators over time before making a diagnosis. By accumulating evidence across multiple time steps and sensor readings, the system prepares diagnostic information in advance, enabling timely detection even when individual fault signs are hidden or ambiguous.
Solution Approach 2:
The patent adds the temporal dimension to fault detection by aggregating observations over time. This transforms the problem from detecting hidden faults at a single moment to analyzing fault patterns across multiple time steps, making previously unobservable faults detectable through their temporal evolution.
3Measurement precision
If multiple faults are considered simultaneously, then diagnostic accuracy improves, but the ambiguity in fault-diagnosis mapping increases
Solution Approach 1:
The patent segments the fault reasoning process into distinct components: fuzzy set definition, mapping table creation, temporal aggregation, and threshold-based diagnosis. This segmentation breaks down the complex multi-fault reasoning into manageable steps, improving diagnostic accuracy while controlling reasoning complexity through structured processing.
Solution Approach 2:
The patent implements dynamic fault scoring where fault probabilities evolve over time based on accumulating evidence. Instead of static analysis, the system continuously updates fault assessments as new observations arrive, enabling accurate multi-fault diagnosis while adapting reasoning complexity to the actual diagnostic needs at each time step.
Data Source
AI summary
A system that transforms information into fuzzy observable states. These states may be matched against a mapping table which indicates which observable state admits or excludes particular faults. This information may be processed over time when in each time instant the admitted or excluded faults are used for updating the rate for each fault.