Systems and methods for detecting a fault in a climate control system
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Solution Overview
Problem
Climate control systems, such as HVAC systems, face challenges in detecting faults that lead to increased runtime to achieve a temperature setpoint, resulting in reduced energy efficiency and user discomfort, with faults often being intermittent and difficult to detect due to temperature-dependent and geographic location-specific conditions.
Innovation Solution
A method that compares the expected runtime to achieve a temperature setpoint, based on the design temperature of a geographic location, with the actual runtime of an HVAC system, to detect faults and issue alerts when the actual runtime exceeds the expected runtime, thereby avoiding false alerts and identifying capacity losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the HVAC system runtime is extended to achieve temperature setpoint, then the temperature control accuracy is improved, but the energy consumption increases and system efficiency deteriorates
Solution Approach 1:
The system performs preliminary calculations of expected runtime based on geographic location design temperatures and current conditions before actually running the HVAC system. This allows the system to predict whether extended runtime will be needed and take preventive measures, such as adjusting setpoints or notifying users, before excessive energy consumption occurs.
Solution Approach 2:
The system continuously monitors actual runtime and compares it against expected runtime calculations. When actual runtime exceeds expected runtime, the system provides feedback through alerts to users or technicians about potential faults. This feedback loop enables early detection of efficiency problems without allowing excessive energy consumption to occur.
2Measurement precision
If the HVAC system runtime is extended to achieve temperature setpoint, then the temperature control accuracy is improved, but the productivity decreases
Solution Approach 1:
The system implements a feedback mechanism that compares actual runtime with expected runtime calculated from geographic design temperatures. When deviations indicate potential faults, the system generates alerts for early intervention, preventing prolonged inefficient operation and maintaining overall system productivity.
Solution Approach 2:
By calculating expected runtime in advance based on geographic location and current conditions, the system can identify potential efficiency problems before they significantly impact productivity. This preliminary assessment allows for proactive maintenance scheduling that minimizes disruption to system performance.
3Device complexity
If fault detection methods are simplified, then the device complexity is reduced, but the measurement precision of fault detection deteriorates
Solution Approach 1:
The system uses readily available data from the HVAC system's own operation (runtime, temperature readings) combined with publicly available geographic information (design temperatures from weather data) to perform self-diagnosis. This self-service approach avoids complex external monitoring equipment while maintaining accurate fault detection through intelligent comparison of expected versus actual performance.
Solution Approach 2:
The system detects faults by monitoring changes in operational parameters, specifically comparing actual runtime against expected runtime calculated from geographic design temperatures. This parameter-based detection method is simple to implement but highly effective at identifying capacity losses and performance degradation without requiring complex diagnostic equipment.
Data Source
AI summary
Methods and related systems of detecting a fault in a heating, ventilation, and air conditioning (HVAC) system are disclosed. In an embodiment, the method includes determining an expected runtime of the HVAC system required to achieve a temperature setpoint in a comfort zone of the HVAC system following a triggering event, wherein the expected runtime is based on a design temperature of a geographic location at which the HVAC system is installed. Additionally, the method includes monitoring a current runtime of the HVAC system elapsed following the triggering event, and comparing the expected runtime with the current runtime of the HVAC system elapsed following the triggering event.