Wyniki wyszukiwania - BazTech

1

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

Manikandan P., Geetha M.

Archives of Control Sciences

|

2014

|

Vol. 24, no. 3

271--287

EN

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

2

Obserwatory o nieznanym wejściu w diagnostyce układu dwóch zbiorników

Pawłowska M., Witczak M.

Diagnostyka

|

2006

|

nr 3(39)

109-116

PL

Przedmiotem pracy jest zaproponowanie systemu detekcji i lokalizacji uszkodzeń układu dwóch zbiorników z zastosowaniem rozszerzonych obserwatorów o nieznanym wejściu. W szczególności, pokazuje się jak rozwiązać problem lokalizacji uszkodzeń z zastosowaniem banku rozszerzonych obserwatorów o nieznanym wejściu, gdzie każdy obserwator wrażliwy jest na wszystkie uszkodzenia oprócz jednego. W końcowej części pracy przedstawia się rezultaty symulacji komputerowych potwierdzające skuteczność proponowanego rozwiązania.

EN

The main objective of the paper is to propose a fault detection and isolation system for a two-tank system using extended unknown input observers. In particular, it is shown how to solve the fault isolation problem with a bank of extended unknown input observers where each of the observers is sensitive to all but one faults. The final part of the paper presents the computer simulation results that confirm the effectiveness of the proposed approach.

3

Neural Network Fault Detection System for Dynamic Processes

Korbicz J., Patan K., Obuchowicz A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2001

|

vol. 49, nr 2

301-321

EN

The neural model-based Fault Detection and Isolation (FDI) system for dynamic non-linear processes is considered. The emphasis is placed upon the use of Artificial Neural Networks (ANN's) for residual generation. The proposed network is constructed with the Dynamic Neuron Model (DNM) which contains local memory. Similar to server based schemes, a network is applied to build the nominal and fault models of the investigated system. The output residuals between the process and the models bank are use to detect and identify faults in the system. The modelling efficiency based on the multilayer feedforward Network of Dynamic Neurons (NDN) is compared with the Elman and recurrent network with outside feedbacks. Finally, the NDN and the cascade NDN architectures are applied to build Neural-Residual Generators (NRG) of the two tank system.

4

Dynamic Neural Networks for Process Modelling in Fault Detection and Isolation Systems

Korbicz J., Patan K., Obuchowicz A.

International Journal of Applied Mathematics and Computer Science

|

1999

|

Vol. 9, no 3

519-546

EN

A fault diagnosis scheme for unknown nonlinear dynamic systems with modules of residual generation and residual evaluation is considered. Main emphasis is placed upon designing a bank of neural networks with dynamic neurons that model a system diagnosed at normal and faulty operating points.To improve the quality of neural modelling, two optimization problems are included in the construction of such dynamic networks: searching for an optimal network architecture and the network training algorithm. To find a good solution, the effective well-known cascade-correlation algorithm is adapted here. The residuals generated by a bank of neural models are then evaluated by means of pattern classification. To illustrate the effectiveness of our approach, two applications are presented: a neural model of Narendra's system and a fault detection and identification system for the two-tank process.

5

Application of Sensitivity Theory to Fuzzy Logic Based Fdi

Dalton T., Klotzek P., Frank P. M.

International Journal of Applied Mathematics and Computer Science

|

1999

|

Vol. 9, no 3

619-636

EN

This paper describes an application of sensitivity theory to the analysis of a certain class of fuzzy systems which can be used for fault detection and isolation (FDI). The work is divided into three main tasks. The first is the mathematical representation of some class of fuzzy systems. This is followed by an application of sensitivity theory to fuzzy systems based on the approach detailed in the first part. Finally, this method is applied to a fuzzy fault diagnosis scheme for the two-tank system, and the results compared with those achieved by the application of sensitivity theory to a non-fuzzy diagnosis scheme for the same system. Simulation results for the fuzzy and non-fuzzy fault diagnosis schemes are presented, which verify the results obtained via the application of sensitivity theory.