An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems

Daigle, Matthew; Koutsoukos, Xenofon; Biswas, Gautam

doi:10.1007/978-3-540-78929-1_47

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…Yet, it is equally important to consider other factors when multiple faults occur. Addressing the multiple fault problem is crucial because solely focusing on single faults can lead to inaccurate or unsuccessful diagnoses when multiple faults are present [14], [15]. The study [16] presents a new algorithm for generating test patterns to detect multiple faults in a combinational logic network, while Dorantes et al [17] proposed a novel methodology for multiple-fault detection and identification.…”

Section: Introductionmentioning

confidence: 99%

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

Fadzail,

Zali,

Mid

2024

IJECE

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The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

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Section: Introductionmentioning

confidence: 99%

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

Fadzail,

Zali,

Mid

2024

IJECE

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“…The fault isolation methodology uses a qualitative fault signature scheme to generate diagnostic conclusions. This paper develops a combined qualitative fault isolation and identification methodology using the framework presented in [Mosterman and Biswas, 1999], and further developed in [Daigle et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

Fault Isolation for Spacecraft Systems: An Application to a Power Distribution Testbed

Carl

Mack

Tantawy

et al. 2012

IFAC Proceedings Volumes

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“…On the other hand, in the DX approach, some authors have proposed alternative ways to diagnosing hybrid systems as the hybrid bond graph formalism [Narasimhan and Biswas, 2007, Daigle, 2008. Unlike hybrid automata models, pre-enumeration of all system modes, is avoided by generating models at runtime as mode switches occur.…”

Section: Hybrid Systems Diagnosis Approachesmentioning

confidence: 99%

“…A comparison among the main approaches is shown in Table 2.1, where, advantages and limitations Approach (Authors) FDI a) [Mezyani, 2007, Cocquempot et al, 2004] FDI b) [Bayoudh et al, 2008] DX [Daigle, 2008] , 2002, , Heemels et al, 2001. The evolution of an MLD model is governed by linear dynamic equations subject to linear mixed integer inequalities, i.e.…”

Section: Hybrid Systems Diagnosis Approachesmentioning

confidence: 99%

“…Model-based online diagnosis requires quick and robust reconfiguration processes when a mode change occurs, as well as the ability to keep the nominal behavior of the system on track during transient states [Bregon et al, 2012]. The hybrid system behavior can be described by a hybrid automaton model [Hofbaur and Williams, 2004] or the hybrid bond graph formalism [Narasimhan and Biswas, 2007, Daigle, 2008.…”

Section: Introductionmentioning

confidence: 99%

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Methodologies for hybrid systems diagnosis based on the hybrid automaton framework

Vento Maldonado

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Hybrid systems play an important role in the modeling of complex systems since they take into account the interaction between both continuous dynamics and discrete events. Complex systems are subject to changes in the dynamics due to several factors such as nonlinearities, changes in the parameters, disturbances, faults, discrete events and controller actions among others. These facts lead to the need to develop a diagnostic system for hybrid systems improving the diagnostic precision. Hybrid systems allow to combine the classic fault detection and isolation approaches and a diagnoser based on discrete event models. Hence, a design methodology and implementation architecture for diagnosers in the framework of hybrid systems is proposed. The design methodology is based on the hybrid automaton model that represents the system behavior by means of the interaction of continuous dynamics and discrete events. The architecture is composed of modules which carry out mode recognition and diagnostic tasks interacting each other, since the diagnosis module adapts accordingly to the current hybrid system mode. The mode recognition task involves detecting and identifying a mode change by determining the set of residuals that are consistent with the current hybrid system mode. On the other hand, the diagnostic task involves detecting and isolating two type of faults: structural and non-structural faults. In the first case, structural faults are represented by a dynamic model as in the case of nominal modes. Hence they are identified by consistency checking through the set of residuals. In the second case, non-structural faults do not change the structure of the model, therefore, they are identified by a proper residual pattern. %the set of of residuals that can explain this inconsistency. Discernibility is the main property used in hybrid systems diagnosis. Through the concept of discernibility it is possible to predict whether modes changes (faulty or nominal) in the hybrid model can be detected and isolated properly. This concept can be applied in practice, evaluating a set of mathematical properties derived from residual expressions, which can be obtained from input-output models or parity space equations. General properties are derived to evaluate the discernibility between modes in the hybrid automaton model. The diagnoser is built through propagation algorithms developed for discrete models represented by automata. The automaton employed to build the diagnoser for a hybrid system is named behaviour automaton. It gathers all information provided by discernibility properties between modes and observable events in the system, increasing the system diagnosability. % in the system. Diagnosis for hybrid systems can be divided in two stages: offline and online. Moreover, it can be carried out twofold: in a non-incremental and an incremental form. In the non-incremental form, algorithms are executed taking into account global models, unlike incremental form that leads to building the useful parts of the diagnoser, only developing the branches that are needed to explain the occurrence of incoming events. The resulting diagnoser adapts to the system operational life and it is much less demanding in terms of memory storage than building the full diagnoser offline. The methodology is validated by the application to a case study based on a representative part of the Barcelona sewer network by means of a tool implemented in Matlab.

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An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems

Cited by 8 publications

References 18 publications

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

Fault Isolation for Spacecraft Systems: An Application to a Power Distribution Testbed

Methodologies for hybrid systems diagnosis based on the hybrid automaton framework

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