Air Conditioning Cycle Diagnosis Without Model-Specific Setup
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Solution Overview
Problem
Conventional air conditioning apparatuses require time-consuming input of model-specific information, including piping length and height differences, and fail to accurately diagnose refrigerant leaks or blockages due to factors like fin deterioration and wind influence, especially when equipped with surplus refrigerant storage elements, leading to incorrect or delayed detection and increased costs.
Innovation Solution
An air conditioning apparatus that learns and stores refrigerating cycle characteristics at normal conditions, allowing for accurate diagnosis of normality or abnormality under any installation or environmental conditions without requiring input of model-specific details, using temperature and pressure detection parts, a control system, and a calculation comparison part to judge refrigerant leaks and blockages, even in systems with surplus refrigerant storage, without additional detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model name information, piping length, and height difference are input manually after installation, then cycle simulation can be performed accurately, but installation and maintenance time increase significantly
Solution Approach 1:
The system performs preliminary learning of refrigerating cycle characteristics during normal operation before diagnosis is needed. The learning section accumulates temperature, pressure, and operation amount data over time, so when diagnosis is required, the comparison can be made immediately without manual input of installation parameters, thus resolving the time loss contradiction
Solution Approach 2:
The air conditioning apparatus automatically learns and stores its own operational characteristics without external intervention. The learning section uses data from the apparatus's own sensors and operation data to build its reference profile, eliminating the need for manual input of model information, piping length, and height difference, thereby maintaining diagnosis accuracy while reducing installation and maintenance time
2Productivity
If conventional cycle simulation is used without considering environmental factors, then calculation is simple, but diagnosis accuracy decreases due to fin deterioration, filter blockage, and wind influence
Solution Approach 1:
The system continuously compares actual operation characteristics with learned normal characteristics and uses this feedback to identify abnormalities. The comparison section analyzes deviations in temperature, pressure, and operation amount ratios, enabling accurate diagnosis that accounts for environmental factors like fin deterioration and filter blockage while maintaining fast diagnosis speed
Solution Approach 2:
The system monitors changes in multiple operational parameters simultaneously (temperature, pressure, operation amount) and compares their ratios and relationships rather than absolute values. This approach allows accurate diagnosis under varying environmental conditions because it detects deviations from the learned characteristic patterns rather than requiring exact matches to theoretical values
3Reliability
If surplus refrigerant storage equipment (accumulator/receiver) is installed, then refrigerant management is improved, but refrigerant leak detection becomes difficult as temperature and pressure remain unchanged
Solution Approach 1:
The system detects refrigerant leaks by analyzing the relationship between operation amount and refrigerating cycle characteristics (temperature and pressure ratios) rather than relying on absolute temperature or pressure values alone. By examining how these parameters change relative to each other and to the learned characteristics, the system can detect leaks even when surplus refrigerant buffers the pressure and temperature changes
4Measurement precision
If ultrasonic sensor is used to detect surplus refrigerant amount in accumulator, then refrigerant leak can be detected, but system cost increases significantly
Solution Approach 1:
The system creates a virtual model of normal refrigerating cycle characteristics by learning from operational data, then compares actual performance against this learned model. This software-based approach replicates the diagnostic functionality that would otherwise require expensive ultrasonic sensors, achieving accurate refrigerant leak detection through data analysis rather than physical detection devices
Solution Approach 2:
The invention replaces the mechanical/physical ultrasonic sensor system with an information-processing system that uses temperature, pressure, and operation amount sensors combined with learning and comparison algorithms. This substitution eliminates the need for expensive specialized detectors while maintaining or improving detection accuracy through comprehensive operational analysis
Data Source
AI summary
By studying or storing refrigerating cycle characteristics of an air conditioning apparatus at the normal time and comparing them with refrigerating cycle characteristics acquired from the air conditioning apparatus at the time of operation, it becomes possible to exactly and accurately diagnose normality or abnormality of the air conditioning apparatus under any installation conditions and environmental conditions, which eliminates operations of inputting a difference between apparatus model names, a piping length, a height difference, etc at the time of apparatus installation. Accordingly, it aims at shortening the time of judging normality or abnormality, and improving the operability. It is characterized by calculating and comparing a measured value (a value of liquid phase temperature efficiency εL (SC/dTc) calculated from temperature information) concerning an amount of a liquid phase part of the refrigerant in the high-pressure-side heat exchanger with a theoretical value (a value of liquid phase temperature efficiency εL (1−EXP(−NTUR)) calculated from the transfer unit number NTUR at refrigerant side).