Compressor Rack Monitoring Using Amp-Draw Fault Modeling
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Solution Overview
Problem
Current commercial refrigeration systems lack the capability to monitor load distribution among individual compressors in a compressor rack and diagnose faults effectively, leading to inefficiencies and increased downtime, as existing methods are limited to whole-rack performance or require manufacturer-specific data.
Innovation Solution
A system that generates a staging matrix from commonly available sensor data to visualize compressor rack operation, enabling drill-down analysis of load distribution and fault detection by modeling steady-state amp draw using Air Conditioning and Refrigeration Institute equations, without requiring additional sensor installations or detailed compressor maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If whole-rack performance monitoring is used, then system-level oversight is achieved, but individual compressor fault detection capability is lost
Solution Approach 1:
The patent segments the compressor rack monitoring into individual compressor-level analysis by tracking each compressor's amp draw separately and comparing it against modeled steady-state values. This segmentation enables drill-down capability to identify which specific compressor is underperforming without requiring a completely separate monitoring system for each unit.
Solution Approach 2:
The patent introduces an intermediary modeling approach using steady-state amp draw models based on suction pressure, discharge pressure, and compressor speed. This intermediary model acts as a reference baseline against which actual compressor performance is compared, enabling fault detection without direct manufacturer data while maintaining individual compressor-level insight.
2Measurement precision
If manufacturer-specific compressor maps are used, then accurate fault diagnostics are achieved, but system adaptability and deployment speed are reduced
Solution Approach 1:
The patent changes the approach from using fixed manufacturer-specific parameters (compressor maps) to dynamically modeling steady-state amp draw based on operating conditions (suction pressure, discharge pressure, compressor speed). This parameter-based modeling approach maintains diagnostic accuracy while enabling universal application across different compressor types without requiring manufacturer-specific data.
Solution Approach 2:
The system performs self-characterization by collecting operational data during commissioning and automatically generating steady-state amp draw models for each compressor. This self-service capability eliminates the need for external manufacturer data while achieving accurate baseline performance values tailored to each specific compressor's actual operation.
3Measurement precision
If additional sensors are installed for detailed monitoring, then measurement precision is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent extracts the necessary monitoring capability from the physical sensor domain into the data processing domain. By using existing sensors (current transformers, pressure sensors) and applying sophisticated analysis through steady-state modeling and amp draw comparison, the system achieves individual compressor-level precision without extracting or installing additional physical sensors on each compressor unit.
Data Source
AI summary
A commercial refrigeration system receives data from a plurality of compressors in a compressor rack, and uses the data to model a steady state amp draw for each of the compressors in the compressor rack. The system receives additional data from the plurality of compressors, and determines a steady state amp draw for each of the compressors from the additional data. The system then compares the amp draw from the additional data with the steady state amp draw model, and identifies a compressor fault based on the comparison of the steady state amp draw from the additional data with the steady state amp draw model.