Closed Loop Fault Diagnosis Based on a Nonlinear Process Model and Automatic Fuzzy Rule Generation

Füssel, Dominik; Ballé, Peter; Isermann, Rolf

doi:10.1016/s1474-6670(17)42426-8

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…TSK fuzzy modeling is a very powerful tool for function approximation due to its capability to explain nonlinear relation using a relatively low number of simple rules. In fact, TSK fuzzy model is a general non-linear approximator that can be applied to every continuous mapping, and on the other hand it is a piecewise linear model that is relatively easy to interpret [17] and whose linear sub-models can be exploited for control and fault detection [1].…”

Section: Tsk Fuzzy Modelingmentioning

confidence: 99%

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Fuzzy cutting force modelling in micro-milling

Ren

Baron

Balazinskia

et al. 2013

Journal of Intelligent &Amp; Fuzzy Systems

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Reliable prediction of cutting forces is essential for micromilling. In this paper, a fuzzy cutting force modelling method based on subtractive clustering method filters the noise and estimates the instantaneous cutting forces using observations acquired by sensors during cutting experiments. In the experimental case study, four data sets of micromilling cutting force are used. Each data set is used to generate a learning system which is tested by the other three data sets. It is proven that the proposed algorithm has the capability to filter and model the cutting force in spite of uncertainties in the micromilling process.

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Section: Tsk Fuzzy Modelingmentioning

confidence: 99%

“…3. First, the cutting force components F x , F y and F z from the x, y, z direction are used to compute the main force using (1). Then training of the system is performed using fuzzy approach based on subtractive clustering method.…”

Section: Fuzzy Evaluation Strategymentioning

confidence: 99%

Fuzzy cutting force modelling in micro-milling

Ren

Baron

Balazinskia

et al. 2013

Journal of Intelligent &Amp; Fuzzy Systems

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“…This approach has been introduced in [41,19] (see also [5]. The fuzzy functional model approximates a nonlinear dynamic process by x piece-wise linear models.…”

Section: Fuzzy Functional Residual Generationmentioning

confidence: 99%

Modelling for Fault Detection and Isolation versus Modelling for Control

Frank¹,

García²,

Köppen-Seliger³

2001

Mathematical and Computer Modelling of Dynamical Systems

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The main purpose of this paper is to emphasize the particularities of models needed for modelbased fault detection and isolation (FDI) in contrast to the models used for control. Of special interest is the question of complexity of the model, which is of great importance for the practical implementation. This, of course, depends basically on the given situation such as the kind of plant, the measurements, the kind and number of faults to be detected and the demands for fault isolation and robustness. However, the paper shows that diagnostic models, in contrast to the wide-spread opinion that those have always to be more complex than the functional models for control, may be even less complex, because they are restricted to only those parts of the system in which the faults occur. The issue of model complexity is discussed in terms of different model-based FDI approaches ± analytical, knowledge-based and data-based. The ideas are illustrated in a case study, where several types of model-based FDI techniques are compared with the same plant, the amira three tank system.

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“…sensitivity and stability conditions of the learning scheme are also studied. Meanwhile, expert systems, fuzzy logic and genetic algorithms have been used in model based FDI (Lopez, Benkhedda, & Patton, 1997;Fussel, Balle, & Isermann, 1997;Passino & Antsaklis, 1988;Laukonen, Passino, Krishnaswami, Luh, & Rizzoni, 1995;Gremling & Passino, 1997).…”

mentioning

confidence: 99%

Fault diagnosis for a turbine engine

Diao

Passino

2000

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334)

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Fault detection and diagnosis for jet engines is complicated by the presence of engine-to-engine manufacturing differences and engine deterioration during normal operation, the complexity of an accurate engine model, and our inability to directly measure certain engine variables. Here, we work with a sophisticated component level model (CLM) simulation of a turbine engine (the General Electric XTE46) that can simulate the effects of manufacturing and deterioration differences, in addition to a variety of faults. To develop a fault diagnosis system we begin by using the CLM to generate data that is used by a Levenberg-Marquardt method to train a Takagi-Sugeno fuzzy system to represent the engine. The resulting nonlinear model provides a reasonably accurate representation of manufacturing differences, engine deterioration, and fault effects. We use multiple copies of this nonlinear model, each representing a different fault, to generate error residuals by comparing them to the engine output. In fact, we manage the composition of the set of models with a ''supervisor'' that ensures that the appropriate models are on-line, and processes the error residuals to detect and identify faults. Robustness and fault sensitivity of the proposed approach are studied in the paper and the component model level simulation of the XTE46 engine is used to illustrate the effectiveness of the fault diagnosis scheme. r

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Closed Loop Fault Diagnosis Based on a Nonlinear Process Model and Automatic Fuzzy Rule Generation

Cited by 17 publications

References 5 publications

Fuzzy cutting force modelling in micro-milling

Fuzzy cutting force modelling in micro-milling

Modelling for Fault Detection and Isolation versus Modelling for Control

Fault diagnosis for a turbine engine

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