HVAC Controller Configuration Inference for Automated Fault Detection
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Solution Overview
Problem
Small to medium-sized HVAC contractors face challenges in efficiently managing and interpreting large volumes of data for building comfort and equipment health, leading to inefficient alert systems and increased operational burdens due to complex workflows and lack of common standards.
Innovation Solution
A lightweight alerting system that automatically infers equipment details from controller configurations to trigger relevant fault detection algorithms, providing timely and actionable notifications, and dynamically adjusts gateway poll rates based on application needs, ensuring efficient data exchange while maintaining comfort control and capacity monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fault detection algorithms are manually configured for each HVAC equipment type, then detection accuracy is improved, but system complexity and operational burden increase significantly
Solution Approach 1:
The system automatically infers equipment details from controller configuration data without requiring manual intervention. The cloud subsystem autonomously matches controller configurations to equipment types and selects appropriate fault detection algorithms, enabling the system to serve itself rather than requiring contractor configuration for each equipment type.
Solution Approach 2:
The system pre-defines fault detection algorithms and their associated trigger conditions for various HVAC equipment types in advance. These algorithms are stored in the cloud subsystem and automatically activated based on inferred equipment details, eliminating the need for on-site configuration during installation or commissioning.
2Reliability
If comprehensive monitoring of all HVAC equipment is implemented, then system reliability is improved, but false positives and alert fatigue increase
Solution Approach 1:
The system applies different monitoring strategies and fault detection algorithms tailored to specific equipment types and their operational characteristics. By inferring equipment details from controller configurations, the system customizes the monitoring approach for each piece of equipment rather than applying a uniform monitoring scheme, thereby reducing false positives while maintaining comprehensive coverage.
3Loss of information
If continuous data exchange between gateway and cloud subsystem is maintained, then data freshness is improved, but network bandwidth and energy consumption increase
Solution Approach 1:
The gateway dynamically adjusts its data polling interval based on application needs and system conditions. Instead of continuous data exchange, the gateway periodically requests data from the cloud subsystem at optimized intervals, reducing network traffic and energy consumption while maintaining sufficient data freshness for fault detection purposes.
Data Source
AI summary
A program for light commercial building system (LCBS) solutions. Solutions and other systems may incorporate lightweight alerting service, auto-adjustment of gateway poll rates based on the needs of various consuming applications, detecting loss of space comfort control in a heating, ventilation and air conditioning (HVAC) system, HVAC capacity loss alerting using relative degree days and accumulated stage run time with operational equivalency checks, and HVAC alerting for loss of heat or cool capacity using delta temperature and dependent system properties. Also, incorporated may be triggering s subset of analytics by automatically inferring HVAC equipment details from controller configuration details, ensuring reliability of analytics by retaining logical continuity of HVAC equipment operational data even when controllers and other parts of the system are replaced, and an LCBS gateway with workflow and mechanisms to associate to a contractor account.