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 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.

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 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

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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