Monitoring and optimizing HVAC system
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Solution Overview
Problem
Current HVAC fault detection and diagnosis techniques lack online detection and localization capabilities, leading to inefficiencies in energy usage, user comfort, and operational costs, as they do not utilize adaptive tuning parameters or real-time analytics to monitor and address anomalies effectively.
Innovation Solution
A computer-implemented method using thermodynamic models and data analytics to create an ideal thermodynamic model for HVAC zones, analyzing sensor data to detect and localize anomalies, and dynamically adjust settings to maintain optimal performance, incorporating machine learning and adaptive tuning parameters for real-time monitoring and fault notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault detection techniques are used, then system simplicity is maintained, but online detection and localization capabilities are lacking
Solution Approach 1:
The patent replaces traditional mechanical fault detection methods with a data-driven analytics system that uses sensor data, thermodynamic models, and machine learning algorithms to detect and localize faults in real-time, thereby improving reliability without requiring complex mechanical modifications
Solution Approach 2:
The patent introduces an intermediary analytics layer that sits between the HVAC system components and the control system. This layer processes sensor data, compares it against thermodynamic models, and generates fault diagnoses, enabling online detection and localization without directly modifying the core HVAC components
2Use of energy by moving object
If real-time analytics and adaptive tuning parameters are implemented, then energy efficiency is improved, but computational requirements and system complexity increase
Solution Approach 1:
The patent implements adaptive tuning parameters that dynamically adjust model and controller characteristics based on real-time operating conditions. This allows the system to optimize energy efficiency across varying loads and environmental conditions while managing computational complexity through adaptive rather than static approaches
Solution Approach 2:
The patent changes physical and operational parameters of the thermodynamic models and control algorithms based on real-time sensor data and operating conditions. This enables the system to adapt to different scenarios and optimize energy efficiency without requiring complete redesign of the control architecture
3Measurement precision
If thermodynamic models and data analytics are used for fault detection, then measurement precision is improved, but computational processing requirements increase
Solution Approach 1:
The patent segments the fault detection process into distinct functional modules: data acquisition from sensors, thermodynamic model execution, residual calculation, and fault diagnosis. This segmentation allows each module to be optimized independently and processed efficiently in real-time
Data Source
AI summary
An approach for monitoring, detecting and localizing anomalies of HVAC system by using the combination of thermodynamics models, the energy balance of a zone in steady state, and data analytics is disclosed. The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system. The approach retrieves reading from various sensors and insert the current sensor reading into the ideal model. In the presence of anomalies, the parameters of the model will deviate from their nominal values and an appropriate action can be taken based on the severity of the detected and localized anomalies.