HVAC Performance Mapping for Fault Detection in Multi-Device Systems
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Solution Overview
Problem
Existing HVAC systems, particularly chilled water plants, face challenges in identifying and addressing malfunctions and performance degradation due to the complexity of their components and interrelated operations, leading to difficulties in accurate monitoring and maintenance.
Innovation Solution
Performance mapping is achieved by generating maps that outline expected equipment performance based on operating conditions, using model values and coefficients to capture and compare performance data, enabling more accurate monitoring, maintenance, and energy efficiency optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple devices and parts are used to achieve common cooling functions, then the system can provide comprehensive cooling capability, but it becomes difficult to identify particular sources of malfunction or depreciation
Solution Approach 1:
The system segments the complex HVAC plant into individual device performance profiles, creating separate performance maps for each component (chillers, cooling towers, pumps). This segmentation allows operators to identify which specific device is underperforming by comparing actual performance against its individual baseline, resolving the difficulty of fault identification in multi-device systems.
Solution Approach 2:
The system implements continuous performance monitoring that compares actual device performance against stored performance maps and provides feedback when deviations are detected. This feedback mechanism enables automatic identification of malfunctioning components by highlighting which device's performance diverges from its expected behavior, solving the fault detection problem.
2Measurement precision
If performance monitoring is implemented in complex HVAC systems, then more accurate diagnostics can be achieved, but the complexity of capturing and analyzing data from multiple interrelated components increases
Solution Approach 1:
The system extracts and stores key performance parameters and operating conditions into simplified performance maps during commissioning. By taking out only the essential performance characteristics and storing them as reference baselines, the system achieves accurate diagnostics without requiring complex real-time analysis of all system components, thus reducing data capture complexity while maintaining measurement precision.
Solution Approach 2:
The system transforms complex multi-parameter device performance data into simplified performance maps that relate key performance parameters to operating conditions. By changing the parameter representation from raw sensor data to normalized performance ratios and maps, the system achieves accurate fault detection while simplifying the data capture and analysis complexity.
3Duration of action of stationary object
If performance maps are generated during manufacturing, then equipment performance can be tracked over its life-cycle, but continuous commissioning and monitoring require additional post-installation activities
Solution Approach 1:
The system performs preliminary performance mapping during manufacturing or initial commissioning, establishing baseline performance characteristics before the equipment enters service. This preliminary action captures the device's optimal performance state, enabling long-term life-cycle monitoring without requiring continuous commissioning activities, thus reducing time loss while maintaining monitoring duration.
Data Source
AI summary
Performance mapping of equipment performance parameters by capturing, mapping, and/or structuralizing equipment performance data of a device for installation in a system. This includes generating performance maps which outline the expected feature performance parameter behavior of the equipment based on a set of operating parameters that capture the operating conditions. Each performance parameter on the map is representative of an operating point of specific operating conditions taken at a particular point in time. In one example, a performance parameter can be defined by an individualized set of parameter coefficients which in turn are dependent on instantaneous operating conditions. With the performance maps determined individually for devices as part of the system, and stored along with a time of testing, activities such as continuous commissioning, monitoring and verification, preventative maintenance, fault detection and diagnostics, as well as energy performance benchmarking and long term monitoring can be performed.