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

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.

Mutual Information Based Feature Selection From Data Driven and Model Based Techniques for Fault Detection in Rolling Element Bearings

2011 ◽  
Author(s):  
Karthik Kappaganthu ◽  
C. Nataraj
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Diagnostic System ◽  
Classification Performance ◽  
Data Driven ◽  
Time Frequency ◽  
Bearing Fault ◽  
Model Based ◽  
Rolling Element ◽  
And Performance

This paper proposes a novel technique combining datadriven and model-based techniques to significantly improve the performance in bearing fault diagnostics. Features that provide best classification performance for the given data are selected from a combined set of data driven and model based features. Some of the common data driven techniques from time, frequency and time-frequency domain are considered. For model based feature extraction, recently developed cross-sample entropy is used. The ranking and performance of each of these feature sets are studied, when used independently and when used together. Mutual information based technique is used for ranking and selection of the optimal feature set. Using this method, the contribution to performance and redundancy of each of the data driven features and model based features can be studied. This method can be used to design an effective diagnostic system for bearing fault detection.

Model-Based and Data-Driven Fault Detection Performance for a Small UAV

2013 ◽  
Vol 18 (4) ◽  
pp. 1300-1309 ◽  
Author(s):  
Paul Freeman ◽  
Rohit Pandita ◽  
Nisheeth Srivastava ◽  
Gary J. Balas
Keyword(s):  
Fault Detection ◽  
Detection Performance ◽  
Data Driven ◽  
Model Based ◽  
Small Uav

scholarly journals Data-driven Residual Generation for Early Fault Detection with Limited Data

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Hamed Khorasgani ◽  
Ahmed Farahat ◽  
Chetan Gupta
Keyword(s):  
Fault Detection ◽  
Industrial Applications ◽  
Fault Detection And Isolation ◽  
Data Driven ◽  
Practical Reasons ◽  
Traditional Model ◽  
Sources Of Information ◽  
Environment Change ◽  
Model Based ◽  
Systems Model

Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many complex systems it is not feasible to develop highly accurate models for the systems and to keep the models updated during the system lifetime. Recently, data- driven solutions have received an immense attention in the industrial applications for several practical reasons. First, these methods do not require the initial investment and expertise for developing accurate models. Moreover, it is possible to update and retrain the diagnosers as the system or the environment change over time automatically. Finally, unlike the model-based methods it is straightforward to combine time series measurements such as pressure and voltage with other sources of information such as system operating hours to achieve a higher accuracy. In this paper, we extend the traditional model- based fault detection and isolation concepts such as residuals, and detectable and isolable faults to the data-driven domain. We then propose an algorithm to automatically generate residuals from the normal operating data. We compare the performance of our proposed approach with traditional model-based methods through a case study.

Observer and Data-Driven-Model-Based Fault Detection in Power Plant Coal Mills

2008 ◽  
Vol 23 (2) ◽  
pp. 659-668 ◽  
Author(s):  
P.F. Odgaard ◽  
Bao Lin ◽  
S.B. Jorgensen
Keyword(s):  
Power Plant ◽  
Fault Detection ◽  
Data Driven ◽  
Model Based
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