Air-conditioner system and control method, with first and second abnormality diagnosis
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Solution Overview
Problem
Current air-conditioner systems face challenges in real-time data processing from multiple units distributed across regions, limiting predictive maintenance and early fault detection, as they primarily analyze data only when a failure occurs, rather than proactively.
Innovation Solution
An air-conditioner system with a management server that collects and analyzes data from multiple units, performing both short-term and long-term diagnostics to identify abnormal states, predict failures, and store relevant data for efficient maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple air-conditioners distributed in multiple regions is collected and analyzed in real-time, then predictive maintenance and early fault detection are enabled, but data processing complexity and system resource requirements increase
Solution Approach 1:
The patent segments the diagnosis process into two distinct levels: individual air-conditioner unit diagnosis and group statistical diagnosis. The management server divides incoming data into unit-specific data and aggregated statistical data, processing them through different diagnostic algorithms. This segmentation allows real-time predictive maintenance at the unit level while using statistical analysis at the group level to identify common patterns, thereby managing data processing complexity through structured division of analysis tasks.
Solution Approach 2:
The patent introduces a temporal dimension to diagnosis by comparing current operational data with historical data stored in the database. The management server analyzes data changes over time periods, enabling predictive maintenance by detecting trends and patterns that indicate potential failures before they occur. This dimensional approach transforms static fault detection into dynamic predictive analysis.
2Loss of energy
If data analysis is performed only when failure occurs, then processing resources are conserved, but predictive maintenance capability is lost
Solution Approach 1:
The patent implements partial continuous monitoring by performing full real-time analysis only when abnormalities are detected, while using lighter statistical sampling during normal operation. The management server monitors all units continuously but applies intensive diagnostic algorithms selectively based on detected anomalies or scheduled intervals. This approach reduces overall data processing energy consumption while maintaining predictive maintenance capability for critical failure modes.
3Loss of information
If statistical data from multiple regions is aggregated for analysis, then comprehensive diagnostic insights are obtained, but real-time processing capability is reduced
Solution Approach 1:
The patent segments statistical analysis into unit-level real-time diagnosis and group-level aggregated diagnosis. Individual air-conditioner data is processed immediately for urgent fault detection, while aggregated statistical data from multiple units and regions is analyzed separately to identify common patterns and trends. This segmentation enables both real-time responsiveness and comprehensive diagnostic insights without compromising processing speed for critical unit-level issues.
Data Source
AI summary
The present disclosure provides an air conditioner system that includes a plurality of air-conditioners, and a management server configured to receive air-conditioner data from the plurality of air-conditioners, and to analyze the received air-conditioner data to diagnose a state of the air-conditioners. The management server is configured to perform a first or short term diagnosis of the state of the air-conditioners. The management server to perform a second or long term diagnosis of the state of the air conditioners during a certain period, based on a result of the first diagnosis.