Steady-State Data Detection for Refrigerant Leak and Fault Diagnosis
Solution Overview
Problem
Existing methods for analyzing air source air conditioner operation states struggle to accurately determine steady-state data, leading to potential false determinations of refrigerant leaks and system inefficiencies, as data collected during state changes is not suitable for analysis.
Innovation Solution
A data processing method that resets cumulative changes and detection periods, obtaining and comparing target data to preset thresholds, adjusting detection periods, and determining data as steady-state or unsteady-state based on these conditions, allowing for real-time monitoring and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collected during operation state changes, then data collection frequency is improved, but measurement precision deteriorates due to false determination
Solution Approach 1:
The system performs preliminary actions by continuously monitoring operation state parameters (compressor status, fan speed, valve positions) before data collection to determine whether the air conditioner is in a steady state. This preliminary assessment prevents collection of unreliable data during transitions, resolving the contradiction between collection frequency and precision.
Solution Approach 2:
The system dynamically adjusts data collection based on real-time operation state assessment. Instead of fixed-frequency collection, it adaptively collects data only when steady-state conditions are detected, optimizing both collection efficiency and accuracy by responding to changing system conditions.
2Measurement precision
If data is collected during steady operation state, then measurement precision is improved, but loss of time increases due to waiting for steady state
Solution Approach 1:
The system implements continuous feedback monitoring of operation state parameters to detect when steady-state conditions are achieved. This feedback mechanism triggers data collection automatically upon steady-state detection, minimizing waiting time while ensuring measurement precision through condition-based timing.
Solution Approach 2:
The system uses its own operational parameters (compressor frequency, fan speed, valve positions) as feedback signals to self-determine when steady-state conditions exist. This self-service approach eliminates the need for external timing mechanisms and optimizes data collection timing based on actual system behavior.
3Reliability
If operation state changes are monitored continuously, then reliability of detection is improved, but use of energy increases
Solution Approach 1:
The system applies partial monitoring by selectively assessing only critical operation state parameters (compressor on/off status, fan speed, expansion valve position) rather than continuously analyzing all possible system variables. This partial assessment maintains detection reliability while significantly reducing computational energy consumption.
Data Source
AI summary
A steady-state data processing method includes resetting a cumulative change of target determination data and a data detection period as 0; obtaining the detected target data; calculating the cumulative change of the target data. When the calculated cumulative change of the target data is less than a preset threshold, adjusting the data detection period, or when the calculated cumulative change of the target data is not less than the preset threshold, recording a data detection period. When the recorded data detection period is less than a preset time threshold, determining a target data result obtained in the period as unsteady-state data of the device; or when the recorded data detection period is not less than the preset time threshold, determining the target data obtained in the data detection period as steady-state data.