Systems and methods for adjusting operation of a building management system based on determination whether a building equipment is in steady state
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Solution Overview
Problem
Current building management systems (BMS) face challenges in reliably detecting steady states for equipment operations involving multiple variables, which is essential for accurate performance prediction and fault avoidance, especially in low-cost and minimally supervised industries like HVAC, where existing methods are costly and inefficient.
Innovation Solution
A BMS with a predictive diagnostics system that recursively updates mean and variance of monitored variables to identify steady or transient states, using a combination of slope and second derivative analysis to determine state transitions, allowing for adjusted equipment operation and performance modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical steady state detection methods are applied to multiple variables, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent segments the steady state detection process by separating the analysis of multiple variables into individual variable assessments. Each variable is evaluated independently using the same detection algorithm, avoiding the need for complex multi-variable analysis systems while maintaining detection accuracy.
Solution Approach 2:
Instead of detecting steady state by analyzing complex relationships between multiple variables simultaneously, the patent inverts the approach by detecting when each variable individually reaches steady state. The system determines that the overall system is in steady state when all individual variables are in steady state, simplifying the detection mechanism.
2Measurement precision
If typical steady state detection methods are applied to multiple variables, then measurement precision is improved, but loss of time increases due to parameter tuning requirements
Solution Approach 1:
The detection algorithm is designed to be self-adjusting and adaptive, automatically calibrating to each variable's characteristics without requiring manual parameter tuning. The system learns from the data it collects and adjusts its detection thresholds and parameters automatically, eliminating time-consuming manual configuration.
Solution Approach 2:
The patent employs detection parameters that can dynamically adapt to different variables and operating conditions. Rather than requiring fixed parameters to be manually tuned for each application, the system modifies its detection parameters based on the specific variable being analyzed and the observed operational patterns.
3Measurement precision
If typical steady state detection methods are applied to multiple variables, then measurement precision is improved, but ease of operation deteriorates due to minimal operational supervision
Solution Approach 1:
The system performs self-diagnosis and automatic steady state detection without requiring operator intervention or supervision. The algorithm autonomously monitors variables, detects steady state conditions, and provides outputs that can be directly used for control decisions, making the system easy to operate despite its analytical capabilities.
4Reliability
If steady state detection is implemented for accurate performance prediction, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the reliability improvement function into discrete steady state detection events. Rather than implementing a continuously complex prediction system, the system reliably predicts performance by detecting steady state conditions at appropriate intervals and using those detection results for performance assessment.
Data Source
AI summary
A building management system includes connected equipment and a predictive diagnostics system. The connected equipment is configured to measure a plurality of monitored variables. The predictive diagnostics system includes a communications interface, a steady state detector, a controller. The communications interface is configured to receive samples of the monitored variables from the connected equipment. The steady state detector is configured to recursively update a mean and a variance of the samples each time a new sample is received, identify whether each of the samples reflects a steady state or a transient state of operation of the connected equipment using the mean and the variance, and associate each of the samples to the steady state or the transient state as identified. The controller is configured to adjust an operation of the connected equipment based on the steady state or the transient state as identified.