Fuzzy genetic modelling of air-conditioning systems for fault detection and diagnosis

2004 ◽  
Author(s):  
Chi-yau Kung
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Fault Detection And Diagnosis ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
Genetic Modelling ◽  
Fuzzy Genetic

scholarly journals Fault Detection and Diagnosis of Air Handling Unit: A Review

2019 ◽  
Vol 255 ◽  
pp. 06001 ◽  
Author(s):  
Cheng Yew Leong
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Commercial Building ◽  
Air Handling Unit ◽  
Operations And Maintenance ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
And Control

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

Applying Static Fault Detection and Diagnosis Methods to Transient Air Conditioning Data Using an Equilibrium Prediction

2019 ◽  
Author(s):  
Austin Rogers ◽  
Fangzhou Guo ◽  
Bryan Rasmussen
Keyword(s):  
Steady State ◽  
Fault Detection ◽  
Equilibrium Point ◽  
Air Conditioning ◽  
Fault Detection And Diagnosis ◽  
Thermal Mass ◽  
Static Models ◽  
Transient Data ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems

Abstract Fault detection and diagnosis methods for air conditioning systems typically apply static models after filtering out transient data using a steady state filter. However, air conditioning systems operating in the field often do not achieve a meaningful steady state and therefore the models cannot be applied because only transient data is available. This paper proposes a solution to this problem by predicting the equilibrium point using an exponential regression. The transient response of many system parameters such as cooling capacity, airflow, and refrigerant mass flow may be approximated as a first order dynamic response because the thermal mass in the system dominates other higher order dynamics. The best-fit for a decaying exponential will therefore yield a prediction for the equilibrium point, and static models may then be applied, thus enabling the use of static models with transient data. The method’s performance is quantified using both randomly generated data (Monte Carlo simulations) and the measured response of a field-operating system during both fault-free and faulty operation.

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