HVAC Fault Diagnosis Using Anomaly Scores for Rapid Root Cause Detection
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Solution Overview
Problem
Complexity and time sensitivity in troubleshooting HVAC and refrigeration systems lead to inefficiencies in fault detection and diagnosis, particularly in commercial settings where delays can result in costly losses, such as spoiled food due to temperature fluctuations.
Innovation Solution
A method and system that utilize a baseline model built using Singular Value Decomposition and Probabilistic Latent Semantic Analysis to analyze current and historical data from HVAC systems, calculating an anomaly score to automatically diagnose faults and provide recommended corrective actions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting is performed by technicians or remote service centers, then diagnostic accuracy can be maintained, but the time required for fault detection and diagnosis increases significantly
Solution Approach 1:
The system enables self-diagnosis through automated anomaly detection and root cause identification algorithms that analyze sensor data without requiring external technician intervention. The monitoring system automatically detects faults, determines root causes, and generates diagnostic reports, allowing the climate system to service itself diagnostically while maintaining high accuracy through sophisticated analysis algorithms.
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated computational analysis. Instead of technicians physically inspecting systems or remotely analyzing data manually, the system uses automated algorithms including anomaly detection, root cause analysis, and machine learning models to perform diagnostic functions, substituting human mechanical processes with computational automation.
2Quantity of substance
If multiple climate systems are monitored simultaneously, then comprehensive coverage is achieved, but the capacity of monitoring systems is strained and diagnosis speed decreases
Solution Approach 1:
The monitoring system divides the analysis of multiple climate systems into independent parallel processing streams. Each climate system's data is analyzed separately through automated algorithms, allowing simultaneous monitoring of numerous systems without creating bottlenecks. The segmentation of diagnostic tasks enables scalable monitoring where adding more systems does not proportionally increase processing time for individual systems.
Solution Approach 2:
Automated computational algorithms replace manual monitoring processes, enabling the system to handle multiple climate systems simultaneously without strain. The automated anomaly detection and root cause analysis systems process data from numerous systems in parallel, maintaining diagnosis speed even as the number of monitored systems increases, something that would be impossible with manual technician intervention.
3Measurement precision
If complex troubleshooting procedures are used to accurately diagnose faults, then diagnostic precision is improved, but the ease of operation and speed of troubleshooting deteriorates
Solution Approach 1:
The system performs complex diagnostic analysis automatically without requiring operator intervention or expertise. The automated root cause analysis algorithms execute sophisticated troubleshooting procedures independently, generating accurate diagnostic results that would otherwise require complex manual analysis, thereby simplifying operation while maintaining high diagnostic precision.
Solution Approach 2:
The patent introduces an automated diagnostic system as an intermediary between the climate system and the operator. This intermediary performs complex analysis tasks including anomaly detection, pattern recognition, and root cause identification, translating complex sensor data into simple diagnostic conclusions that are easy for operators to interpret and act upon.
Data Source
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AI summary
A method for diagnosing a fault condition in a climate system is disclosed and a computer program product for doing the same. The climate system may be an HVAC system. The method comprises receiving current data from a climate system in a fault condition, calculating an anomaly score for the climate system from a first set of transition probabilities based on the current data and a second set of transition probabilities based on the climate system operating in a normal condition, and generating automatically a diagnosis of a first problem causing the fault condition when the anomaly score is above a predefined threshold. In an embodiment, the current data may include a plurality of operational Parameters of the climate system.