Active Fault Detection in HVAC Systems Using Parameter Modification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Passive fault detection methods in HVAC systems often result in false negatives or false positives due to the classification of system behavior near decision boundaries, leading to unnecessary technician dispatches or missed faults.
Innovation Solution
An active fault detection system that uses a processor and memory to obtain system data, determine fault probabilities, send control signals to modify system parameters, and generate service schedules, thereby amplifying fault observability and reducing false predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive fault detection methods are used to observe HVAC system behavior and compare with pre-set values, then the system can classify faults, but false negatives or false positives occur due to system behavior near decision boundaries
Solution Approach 1:
The system actively modifies control parameters (such as temperature setpoints, fan speeds, or valve positions) to change system operating conditions. This parameter modification causes the system to transition between different operational states, making fault conditions more distinguishable from normal variations and reducing false predictions near decision boundaries
Solution Approach 2:
The system performs preliminary fault detection assessments using passive observation, and when uncertainty is detected (system behavior near decision boundaries), it proactively initiates control parameter modifications before making a final fault classification. This preliminary action allows the system to gather additional discriminatory data under modified conditions
2Measurement precision
If control parameters are modified to amplify fault observability, then fault detection accuracy improves, but system operation is temporarily disrupted
Solution Approach 1:
Control parameter modifications are implemented as periodic, temporary actions rather than continuous changes. The system modifies parameters for a limited duration sufficient to elicit observable fault responses, then restores normal operation. This periodic approach maintains ease of operation while achieving improved fault detection accuracy
Solution Approach 2:
The system dynamically adjusts control parameters based on real-time fault probability assessments. When fault probability is high, parameters are modified to enhance observability; when fault probability is low or resolved, parameters return to normal operational values. This dynamic adaptation balances fault detection needs with operational continuity
Data Source
AI summary
An active fault detection system for a heating, ventilation, and air conditioning system is disclosed. In some embodiments, the system comprises at least one processor; and memory storing instructions, when executed cause the system to: obtain system data related to operation of one or more components of the HVAC system; determine a fault probability based on the system data, the fault probability indicating a probability of a fault in the HVAC system; send a control signal to a component of the HVAC system to modify one or more control parameters of the component responsive to the fault probability being within a fault threshold range, the threshold range having an upper and lower threshold values; update the determined fault probability based on updated system data resulting from modifying the one or more control parameters; and generate a service schedule responsive to the updated fault probability being above the upper threshold value.