A survey and classification of incipient fault diagnosis approaches. (January 2021)
- Record Type:
- Journal Article
- Title:
- A survey and classification of incipient fault diagnosis approaches. (January 2021)
- Main Title:
- A survey and classification of incipient fault diagnosis approaches
- Authors:
- Safaeipour, H.
Forouzanfar, M.
Casavola, A. - Abstract:
- Abstract: Incipient faults almost occur gradually at a low rate in systems and usually are unnoticeable during their early stages. If diagnostic tools or proper monitoring systems ignore them, they could not be detectable until their effects become severe and cause catastrophic damages to systems. This paper presents a survey on model-based (incipient) fault diagnosis approaches to show the significance of the incipient faults diagnosis in nonlinear closed-loop systems and, by taking a glance through data-based incipient fault diagnosis advancements, a picture of their present state of the art is also briefly discussed for completeness. Moreover, a classification of the most used state estimation filters is also provided. Consequently, recent works on incipient fault diagnosis approaches are reviewed, and an incipient fault diagnosis case study is investigated for a discrete-time nonlinear open-loop system affected by stochastic noise and disturbances. Specifically, a numerical example of a closed-loop three-tank system is considered, and simulations are accomplished, to demonstrate the inability of open-loop incipient fault diagnosis approaches in detecting incipient faults in the proposed closed-loop system. Graphical abstract: Highlights: Survey and classification of linear/nonlinear and open-loop/closed-loop incipient fault diagnosis approaches. Application-based classification of applied estimation filters in the presence of incipient faults. Problem formulation andAbstract: Incipient faults almost occur gradually at a low rate in systems and usually are unnoticeable during their early stages. If diagnostic tools or proper monitoring systems ignore them, they could not be detectable until their effects become severe and cause catastrophic damages to systems. This paper presents a survey on model-based (incipient) fault diagnosis approaches to show the significance of the incipient faults diagnosis in nonlinear closed-loop systems and, by taking a glance through data-based incipient fault diagnosis advancements, a picture of their present state of the art is also briefly discussed for completeness. Moreover, a classification of the most used state estimation filters is also provided. Consequently, recent works on incipient fault diagnosis approaches are reviewed, and an incipient fault diagnosis case study is investigated for a discrete-time nonlinear open-loop system affected by stochastic noise and disturbances. Specifically, a numerical example of a closed-loop three-tank system is considered, and simulations are accomplished, to demonstrate the inability of open-loop incipient fault diagnosis approaches in detecting incipient faults in the proposed closed-loop system. Graphical abstract: Highlights: Survey and classification of linear/nonlinear and open-loop/closed-loop incipient fault diagnosis approaches. Application-based classification of applied estimation filters in the presence of incipient faults. Problem formulation and solution of a nonlinear closed-loop system for model-based incipient fault diagnosis. Discussion on a incipient fault diagnosis approach; nonlinear open-loop vs. closed-loop systems. … (more)
- Is Part Of:
- Journal of process control. Volume 97(2021)
- Journal:
- Journal of process control
- Issue:
- Volume 97(2021)
- Issue Display:
- Volume 97, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 97
- Issue:
- 2021
- Issue Sort Value:
- 2021-0097-2021-0000
- Page Start:
- 1
- Page End:
- 16
- Publication Date:
- 2021-01
- Subjects:
- Fault diagnosis -- Fault estimation -- Incipient fault -- Incipient fault detection and isolation -- Model-based -- Residual signal -- Stochastic
Process control -- Periodicals
Fabrication -- Contrôle -- Périodiques
Process control
Periodicals
Electronic journals
660.281 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09591524 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.jprocont.2020.11.005 ↗
- Languages:
- English
- ISSNs:
- 0959-1524
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5042.645000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 15543.xml