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An Introduction to Sliding Mode Control
Published in Christopher Edwards, Sarah K. Spurgeon, Sliding Mode Control: Theory and Applications, 1998
Christopher Edwards, Sarah K. Spurgeon
The fundamental purpose of a fault detection and isolation (FDI) scheme is to generate an alarm when an abnormal condition, such as a component malfunction or variation in operating condition, develops in the process being monitored and to identify the source or location. An extensively studied FDI methodology is the observer-based approach (Patton et al., 1989) which analyses residuals formed from the difference between the actual outputs and the outputs from an observer. In the absence of faults, the residuals are designed to be small: once a fault occurs, residuals are intended to react by becoming larger (or greater than some predefined threshold), thus signifying the presence of a fault. These schemes usually employ full-order Luenberger observers in which the gain matrix is designed either to make certain residuals sensitive to certain faults and not others, or else to make the residual vector lie in a specific direction in response to a particular fault, thus enabling the source to be identified. This section considers the use of the sliding mode observer defined in Section 6.3 to reconstruct the fault rather than analyse residuals.
Sensor fault detection and isolation: a game theoretic approach
Published in International Journal of Systems Science, 2018
Hamed Habibi, Ian Howard, Reza Habibi
From industrial safety systems to sustainable plants, fault detection and isolation (FDI) techniques play an essential role to detect and isolate faults in systems as early as possible and to generate the critical information which will be used to remove the fault effect from the overall system and keep the performance at the desirable level till the next prescheduled maintenance procedure (Ren, Ding, & Li, 2017; Song and Guo, 2017). In fact, implementing active fault tolerant control, in which FDI is an important step to prepare the fault information, reduces the shutdown periods and unplanned maintenance, also it can be used to prevent the component faults from degrading further into catastrophic failure and increases system reliability, especially for systems operating in harsh environments, e.g. offshore wind turbines (Habibi, Nohooji, & Howard, 2017b). It is also profitable to utilise the fault information obtained from FDI in manual maintenance approaches (Habibi, Howard, & Habibi, 2017a)
Fault detection and isolation using viability theory and interval observers
Published in International Journal of Systems Science, 2018
Majid Ghaniee Zarch, Vicenç Puig, Javad Poshtan, Mahdi Aliyari Shoorehdeli
Conventional feedback control systems are vulnerable to malfunctions in sensors, actuators or other system components. Therefore, diagnosing which kind of faults are developing is an important task to prevent physical damage and performance degradation. Fault detection and isolation (FDI) could also lead to more reliable and efficient systems (Mohajerpoor, Abdi, & Nahavandi, 2015). In this area of study, a lot of different methods have been proposed in the literature (Gao, Cecati, & Ding, 2015) including observer-based methods (Mondal, 2017; Wang, Yang, & Liu, 2007), parity space (Blesa, Puig, Saludes, & Fernández-Cantí, 2016; Ghaniee Zarch & Aliyari Shoorehdeli, n.d.), parameter estimation (Iurinic, Herrera-Orozco, Ferraz, & Bretas, 2016) and artificial intelligence methods (Li & Yang, 2014).
Diagnostic based on estimation using linear programming for partially observable petri nets with indistinguishable events
Published in International Journal of Systems Science: Operations & Logistics, 2020
Amira Chouchane, Philippe Declerck, Atef Khedher, Anas Kamoun
Fault diagnosis is the process of Fault Detection and Isolation (FDI) which can also include fault identification. Fault detection is a binary decision that decides whether the system is in normal or abnormal operation. Fault isolation consists in identifying the system component (plant equipment as a sensor or actuator, component malfunctioning, etc.) responsible for the occurrence of this fault. Fault identification consists in determining the amplitude and possible evolution of the faults over time.