Refrigeration System Controller with Adaptive Baseline Diagnostics
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Solution Overview
Problem
Refrigeration system users lack the expertise to accurately analyze system performance and detect performance issues, leading to inefficiencies and increased operational costs due to inadequate monitoring and diagnostics.
Innovation Solution
A system controller and method for monitoring and controlling refrigeration or HVAC systems, which includes a compressor rack and condensing unit, to determine power consumption data, compare it with predicted and benchmark values, and generate health indicator scores, as well as implementing features like flood-back discharge temperature management and weather-based capacity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users monitor system performance manually, then they can detect performance issues, but they lack the expertise to accurately analyze system performance and diagnose issues
Solution Approach 1:
The patent introduces an intermediary system comprising a controller and processor that automatically collects, analyzes, and interprets refrigeration system performance data. This intermediary device bridges the gap between raw sensor data and user understanding, providing expert-level diagnostic capabilities without requiring users to possess specialized knowledge. The system processes parameters such as power consumption, temperature, and pressure to generate actionable insights.
Solution Approach 2:
The patent replaces manual expert analysis with an automated electronic system that uses processors and algorithms to diagnose system performance. Instead of relying on human experts to interpret complex refrigeration parameters, the system automatically compares actual performance data against expected ranges and historical patterns, substituting mechanical human expertise with electronic diagnostic capabilities.
2Reliability
If users closely monitor system performance, then they can detect performance issues, but it represents a significant portion of operational costs
Solution Approach 1:
The patent implements a feedback mechanism where the controller continuously monitors system parameters and compares them against expected performance ranges. When deviations are detected, the system provides feedback through alerts or notifications, enabling timely intervention. This feedback loop ensures reliable performance monitoring while optimizing energy consumption by only activating intensive monitoring when anomalies are detected, rather than continuously operating at full monitoring capacity.
Solution Approach 2:
The system performs preliminary diagnostics by continuously tracking key performance indicators and comparing them against baseline data. By detecting early signs of inefficiency or malfunction, the system enables preventive maintenance actions before significant energy waste or system failure occurs. This preliminary detection capability reduces overall operational costs by preventing costly breakdowns and optimizing energy usage patterns.
3Productivity
If the system determines detailed power consumption data and health indicator scores, then it can identify issues and optimize operation, but it increases device complexity
Solution Approach 1:
The patent employs a multi-functional controller that performs diverse tasks including data collection, analysis, comparison with baseline data, health scoring, and optimization recommendations. By consolidating these functions into a single universal device, the system achieves high productivity through comprehensive monitoring and optimization while minimizing the number of separate components needed. The controller can adapt to monitor different parameters (power consumption, temperature, pressure) and serve multiple diagnostic purposes.
Data Source
AI summary
A system and a method are provided including a controller for a refrigeration or HVAC system having a compressor rack with at least one compressor. The controller communicates with a tracking module configured to diagnose health of a compressor in the compressor rack. In response to rated performance data for the compressor being unavailable, the tracking module is configured to generate baseline data for the compressor and to diagnose health of the compressor by comparing operational data of the compressor to the baseline data for the compressor. In response to the rated performance data for the compressor being available, the tracking module is configured to diagnose health of the compressor by comparing the operational data of the compressor to the rated performance data for the compressor.