A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem

Uppal, Faisel J.; Patton, Ron J.; Witczak, Marcin

doi:10.1016/j.conengprac.2005.04.015

Cited by 51 publications

(21 citation statements)

References 9 publications

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“…Indeed, any vector CLf k ∈ col(CE), where 19) can be written as 20) for some non-zero vectorf k . As a consequence,…”

Section: Preventing Fault Decouplingmentioning

confidence: 99%

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Unknown Input Observers and Filters

Witczak

2013

Lecture Notes in Electrical Engineering

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“…Indeed, any vector CLf k ∈ col(CE), where 19) can be written as 20) for some non-zero vectorf k . As a consequence,…”

Section: Preventing Fault Decouplingmentioning

confidence: 99%

“…The proposed solutions can be perceived as an alternative to the Takagi-Sugenobased approach presented, e.g., in Ref. [19], which will be the subject of Chap. 6.…”

mentioning

confidence: 99%

Unknown Input Observers and Filters

Witczak

2013

Lecture Notes in Electrical Engineering

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“…In the sequel, this multiple model is called the heterogeneous multiple model. The previously quoted works have illustrated successful implementations of this structure for modelling (Venkat et al, 2003;Vinsonneau et al, 2005;Orjuela et al, 2006), control (Gawthrop, 1995;Gatzke and Doyle III, 1999;Gregorčič and Lightbody, 2000) or state estimation and diagnostic (Kanev and Verhaegen, 2006;Uppal et al, 2006;Orjuela et al 2008;2009) and have shown its relevance. Hence, this kind of multiple model can be used as an interesting alternative to the homogeneous multiple model.…”

Section: R Orjuela Et Almentioning

confidence: 99%

“…This kind of multiple model, initially proposed by Filev (1991), is reported in the literature under several designations, such as the local-state local model network (Gawthrop, 1995), the multiple local models (Gatzke and Doyle III, 1999;Venkat et al, 2003;Vinsonneau et al, 2005), local model networks by blending the outputs (Gregorčič and Lightbody, 2000;, multiple model for models with a non-common state (Kanev and Verhaegen, 2006), the neuro-fuzzy decoupling multiple model scheme (Uppal et al, 2006) and the recently decoupled multiple model (Orjuela et al, 2006;2009). Despite their different names, these approaches share a similar multiple model structure.…”

Section: R Orjuela Et Almentioning

confidence: 99%

Nonlinear system identification using heterogeneous multiple models

Orjuela¹,

Marx²,

Ragot³

et al. 2013

International Journal of Applied Mathematics and Computer Science

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Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

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“…The main related approaches are: signed directed graph [5], [13], [47], fault tree [23], fuzzy systems [18], [25], [54], qualitative trend analysis [20], [22], [26], [48], mutual information [69], neural networks [12], [17] (neural networks also can be used as observer [64], [51]), artificial immune systems [43], [44], [57], Bayesian networks [65], [70] and the combination of techniques [19], [38].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Stator Current in Induction Machines for Possible Fault Detection: A Fuzzy/Bayesian Approach for the Problem of Time Series Multiple Change Point Detection

et al. 2016

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ABSTRACT. This paper addresses the problem of fault detection in stator winding of induction machine by a multiple change points detection approach in time series. To handle this problem a new fuzzy/Bayesian approach is proposed which differs from previous approaches since it does not require prior information as: the number of change points or the characterization of the data probabilistic distribution. The approach has been applied in the monitoring the current of the stator winding induction machine. The good results obtained by proposed methodology illustrate its efficiency.

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A neuro-fuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem

Cited by 51 publications

References 9 publications

Unknown Input Observers and Filters

Unknown Input Observers and Filters

Nonlinear system identification using heterogeneous multiple models

Monitoring the Stator Current in Induction Machines for Possible Fault Detection: A Fuzzy/Bayesian Approach for the Problem of Time Series Multiple Change Point Detection

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