Air Conditioner Defrosting Control Using Camera and Machine Learning
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Solution Overview
Problem
Conventional air conditioner systems face inefficiencies in detecting and addressing frosting in outdoor heat exchangers, leading to reduced heating performance and unnecessary or delayed defrosting operations due to reliance on simple threshold-based logic, which fails to accurately detect frosting early enough.
Innovation Solution
An air conditioner system employing a defrosting controller with machine learning models, including a Convolutional Neural Network (CNN) for image classification and machine learning models like Random Forest and Deep Neural Networks, to accurately determine frosting and predict frosting timing, enabling timely and efficient defrosting operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple threshold-based logic is used to detect frosting, then the detection method is simple and easy to implement, but the detection accuracy is low and frosting cannot be detected accurately in boundary areas
Solution Approach 1:
The patent replaces the mechanical threshold-based detection system with an image recognition system using a camera and deep learning algorithm. The system captures images of the outdoor heat exchanger and uses neural networks to automatically identify frosting conditions, eliminating the need for manual threshold setting and improving detection accuracy in boundary areas where simple thresholds fail.
Solution Approach 2:
The patent introduces an image processing intermediary layer between the sensor and the control system. Instead of directly using sensor values to determine frosting, the system uses camera images as an intermediary to visually confirm frosting conditions, providing more reliable detection especially in ambiguous boundary cases where threshold logic is uncertain.
2Device complexity
If defrosting operation is performed based on threshold-based logic, then the control logic is simple, but the defrosting operation is performed too late or unnecessarily, degrading operation efficiency
Solution Approach 1:
The patent performs preliminary detection of frosting conditions using image recognition before the frosting significantly impacts heating performance. By continuously monitoring the outdoor heat exchanger with a camera and using deep learning to early-detect frosting, the system can initiate defrosting operations at the optimal time, preventing the need for frequent or prolonged defrosting cycles that degrade efficiency.
Solution Approach 2:
The patent implements a visual feedback loop where the camera continuously monitors the outdoor heat exchanger surface, and the deep learning system provides real-time feedback on frosting conditions. This feedback mechanism allows the control system to make informed decisions about when to initiate and terminate defrosting operations, improving overall operational efficiency by avoiding unnecessary defrosting cycles.
3Ease of operation
If defrosting operation is performed for a certain period of time regardless of surrounding environment, then the defrosting schedule is fixed and simple to control, but the defrosting operation may be unnecessarily performed or performed too late, greatly degrading operation efficiency
Solution Approach 1:
The patent transitions from a static, fixed-time defrosting schedule to a dynamic, condition-based defrosting control system. The camera continuously captures images of the outdoor heat exchanger, and the deep learning system dynamically adjusts the defrosting decision based on real-time visual conditions and environmental factors, allowing the system to adapt to varying operating conditions and avoid unnecessary defrosting operations.
Data Source
AI summary
An air conditioner system is provided that may include an air conditioner including a compressor, an outdoor heat exchanger that performs heat exchange using refrigerant discharged from the compressor, a camera module that photographs the outdoor heat exchanger, a sensor unit including a plurality of sensors, and a communication unit that transmits an image of the outdoor heat exchanger photographed by the camera module and sensor data detected by the sensor unit, and a server including a communication unit that receives the image of the outdoor heat exchanger photographed by the camera module and the sensor data detected by the sensor unit, and a defrosting controller that determines whether the outdoor heat exchanger is frosted based on image data of the outdoor heat exchanger photographed by the camera module, and predicts a frosting timing based on the sensor data detected by the sensor unit.