A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units

2016 ◽  
Vol 127 ◽  
pp. 442-451 ◽  
Author(s):  
Haitao Wang ◽  
Youming Chen
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Multiple Faults ◽  
Robust Fault Detection ◽  
Air Handling Units ◽  
Detection And Diagnosis

A review of fault detection and diagnosis methodologies on air-handling units

2014 ◽  
Vol 82 ◽  
pp. 550-562 ◽  
Author(s):  
Yuebin Yu ◽  
Denchai Woradechjumroen ◽  
Daihong Yu
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Air Handling Units ◽  
Detection And Diagnosis

A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings

2011 ◽  
Vol 90-93 ◽  
pp. 3061-3067
Author(s):  
Hai Tao Wang ◽  
You Ming Chen ◽  
Cary W.H. Chan ◽  
Jian Ying Qin
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Office Buildings ◽  
Model Parameters ◽  
Time Data ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

Online model-based fault detection and diagnosis strategy for VAV air handling units

2012 ◽  
Vol 55 ◽  
pp. 252-263 ◽  
Author(s):  
Haitao Wang ◽  
Youming Chen ◽  
Cary W.H. Chan ◽  
Jianying Qin ◽  
Jinhua Wang
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

scholarly journals Detection and Identification of Loss of Efficiency Faults of Flight Actuators

2015 ◽  
Vol 25 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Daniel Ossmann ◽  
Andreas Varga
Keyword(s):  
Fault Detection ◽  
Surface Damage ◽  
Fault Detection And Diagnosis ◽  
Identification Algorithm ◽  
Detection Techniques ◽  
Fast Detection ◽  
Detection And Identification ◽  
Robust Fault Detection ◽  
Input Signals ◽  
Detection And Diagnosis

Abstract We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units

2014 ◽  
Vol 39 ◽  
pp. 70-83 ◽  
Author(s):  
Ken Bruton ◽  
Paul Raftery ◽  
Peter O'Donovan ◽  
Niall Aughney ◽  
Marcus M. Keane ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Air Handling Units ◽  
Diagnosis Tool ◽  
Detection And Diagnosis
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