scholarly journals Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haitao Wang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Control Chart ◽  
Detection Method ◽  
Time Data ◽  
Air Handling Unit ◽  
Ewma Control Chart ◽  
Robust Fault Detection ◽  
Modeling Errors ◽  
Control Limits

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

A R-EWMA CONTROL CHART FOR MONITORING THE PROCESS RANGE

2005 ◽  
Vol 12 (01) ◽  
pp. 31-49 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA
Keyword(s):  
Optimal Design ◽  
Control Chart ◽  
Design Strategy ◽  
Logarithmic Transformation ◽  
Ewma Control Chart ◽  
Control Limits

This article demonstrates how a three parameter logarithmic transformation combined with an EWMA approach can be used to monitor the range of a process. The computation of the parameters of the logarithmic transformation and the control limits is explained. An easy-to-use table is provided, and illustrative examples are given. The performance of the logarithmic transformation is evaluated under the assumption of normality. An optimal design strategy based on the ARL is presented, and comparisons with other procedures are performed.

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.

scholarly journals Neurofuzzy Approach to Fault Detection of Nonlinear Systems

1999 ◽  
Vol 3 (6) ◽  
pp. 524-531
Author(s):  
Jinglu Hu ◽  
◽  
Kotaro Hirasawa ◽  
Kousuke Kumamaru ◽  
Keyword(s):  
Nonlinear Systems ◽  
Fault Detection ◽  
Linear Systems ◽  
Linear Model ◽  
Basis Function ◽  
Linear Models ◽  
Detection Method ◽  
Local Model ◽  
Model Based ◽  
Robust Fault Detection

This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

scholarly journals Simultaneous Disturbance Compensation and H1/H∞ Optimization In Fault Detection Of UAVs

2018 ◽  
Vol 28 (2) ◽  
pp. 349-362 ◽  
Author(s):  
Hai Liu ◽  
Maiying Zhong ◽  
Rui Yang
Keyword(s):  
Fault Detection ◽  
Flight Control ◽  
Disturbance Compensation ◽  
Flight Control System ◽  
Multiple Sources ◽  
Multiple Disturbances ◽  
Robust Fault Detection ◽  
Modeling Errors ◽  
Time Varying Systems ◽  
Aerial Vehicle

Abstract This paper deals with the problem of robust fault detection (FD) for an unmanned aerial vehicle (UAV) flight control system (FCS). A nonlinear model to describe the UAV longitudinal motions is introduced, in which multiple sources of disturbances include wind effects, modeling errors and sensor noises are classified into groups. Then the FD problem is formulated as fault detection filter (FDF) design for a kind of nonlinear discrete time varying systems subject to multiple disturbances. In order to achieve robust FD performance against multiple disturbances, simultaneous disturbance compensation and H1/H∞ optimization are carried out in designing the FDF. The optimality of the proposed FDF is shown in detail. Finally, both simulations and real flight data are applied to validate the proposed method. An improvement of FD performance is achieved compared with the conventional H1/H∞-FDF.

Model-based and data-driven with multiscale sum of squares double EWMA control chart for fault detection in biological systems

2018 ◽  
Vol 32 (12) ◽  
pp. e3068 ◽  
Author(s):  
Majdi Mansouri ◽  
Mohamed Faouzi Harkat ◽  
Sin Yin Teh ◽  
Ayman Al-khazraji ◽  
Hazem Nounou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Biological Systems ◽  
Sum Of Squares ◽  
Data Driven ◽  
Model Based ◽  
Ewma Control Chart

Robust Fault Detection Method for Uncertain Multivariable Systems

1997 ◽  
Vol 30 (18) ◽  
pp. 91-96 ◽  
Author(s):  
Oh-Kyu Kwon ◽  
Dae-Woo Kim ◽  
Il-Sun Hong
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Multivariable Systems ◽  
Robust Fault Detection

scholarly journals A Robust Fault Detection Method using a Zonotopic Kaucher Set-membership Approach

2018 ◽  
Vol 51 (24) ◽  
pp. 500-507 ◽  
Author(s):  
Stefan Schwab ◽  
Vicenç Puig ◽  
Soeren Hohmann
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Robust Fault Detection ◽  
Set Membership

An $(\tilde{X}/R)$-EWMA CONTROL CHART FOR MONITORING THE PROCESS SAMPLE MEDIAN

2001 ◽  
Vol 08 (02) ◽  
pp. 123-135 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA
Keyword(s):  
Control Chart ◽  
Optimal Parameters ◽  
Sample Median ◽  
Ewma Control Chart ◽  
Control Limits

The method proposed in this paper is a new EWMA type control chart, dedicated to the monitoring of the process sample median [Formula: see text]. Because this control chart uses the range of the process, we call it a [Formula: see text]-EWMA control chart. In this paper, we show how to compute the control limits of this chart, give an illustrative example, describe how to compute the ARL and how to obtain optimal parameters minimizing the out-of-control ARL.

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