Refrigeration Filter Cleaning Detection Using Existing Current Sensors
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Solution Overview
Problem
Existing methods for monitoring filter clogging in refrigeration/freezing facilities, such as freezer showcases, require modifications or additional sensors, making it difficult to implement effective cleaning notifications without disrupting existing installations or requiring environmental sensors.
Innovation Solution
A state estimation apparatus that acquires and stores current information from the power supply part of the refrigeration/freezing facility, evaluates changes in current information over time, and determines whether filter cleaning has been practiced, allowing for simplified monitoring without the need for additional sensors or environmental information acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors or environmental information acquisition devices are installed to monitor filter clogging, then measurement precision and reliability of filter cleaning determination are improved, but device complexity and ease of manufacture deteriorate
Solution Approach 1:
The system uses the refrigeration facility's own existing operational data (current consumption, operation time, temperature) to determine filter clogging status, without requiring external sensors or additional measurement devices. The facility essentially monitors itself using data already being collected for operational control.
Solution Approach 2:
The control apparatus performs multiple functions: it controls the refrigeration cycle, monitors operational parameters, and determines filter cleaning timing - all using the same existing sensor network and control structure, eliminating the need for dedicated filter monitoring hardware.
2Adaptability or versatility
If environmental sensors are installed to acquire store environment information, then adaptability and measurement precision are improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system adapts to different store environments by analyzing the relationship between operational parameters and filter clogging progression specific to each facility, without requiring manual environmental input or configuration. Each facility learns its own clogging pattern based on its operating conditions.
Solution Approach 2:
The control apparatus stores historical operational data and filter cleaning information in advance, building a database that enables accurate determination of cleaning timing based on accumulated patterns rather than real-time environmental sensing.
3Device complexity
If filter cleaning is monitored using existing operational data only, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The system continuously monitors operational parameters and uses feedback from the relationship between operation time, current consumption, and temperature to dynamically determine filter clogging status. The control apparatus adjusts its determination based on accumulated operational patterns and actual system response.
Solution Approach 2:
The control apparatus uses more operational parameters than strictly necessary (current, temperature, operation time combined) to determine filter status, providing redundant information that increases reliability even though each individual parameter might be insufficient alone.
Data Source
AI summary
A state estimation apparatus acquires current information at a power supply part of a refrigeration/freezing facility, derives a degree of change in current information acquired at a state estimation practice time point and derives a degree of change with respect to at least one pair of pieces of current information out of current information acquired at a predetermined time interval before state estimation is practiced, determines whether or not cleaning of the filter of the refrigeration/freezing facility has been practiced, based on the degree of change with respect to at least one pair of pieces of the current information and the degree of change in the current information acquired at the state estimation practice time point.