Wyniki wyszukiwania - BazTech

1

A neural-fuzzy approach for fault diagnosis of hybrid dynamical systems: demonstration on three-tank system

Achbi Mohammed Said, Kechida Sihem, Mhamdi Lotfi, Dhouibi Hedi

Acta Mechanica et Automatica

|

2021

|

Vol. 15, no. 1

1--8

EN

This work is part of the diagnostic field of hybrid dynamic systems (HDS) whose objective is to ensure proper operation of industrial facilities. The study is initially oriented to the modelling approach dedicated to hybrid dynamical systems (HDS). The objective is to look for an adequate model encompassing both aspects (continuous and event). Then, fault diagnosis technique is synthesised using artificial intelligence (AI) techniques. The idea is to introduce a hybrid version combining neural networks and fuzzy logic for residual generation and evaluation. The proposed approach is then validated on three tank system. The modelling and diagnosis approaches are developed using MATLAB/Simulink environment.

2

Fault diagnostics in air intake system of combustion engine using virtual sensors

Komorska I., Wołczyński Z., Borczuch A.

Diagnostyka

|

2018

|

Vol. 19, No. 1

25--32

EN

The method for the fault diagnosing of the air intake system of a gasoline engine, not detected by the onboard diagnostics system in a car, is described in this article. The aim is to detect and identify such faults like changes in sensor characteristic, faults of mass airflow measurement in the intake manifold or manifold leakages. These faults directly affect the air intake system performance that results in engine roughness and a power decrease. The method is based on the generation of residuals on the grounds of differences in indications of the manifold absolute pressure (MAP) and mass air flow (MAF) sensors installed in the car and the virtual, model-based sensors. The empirical model for the fault-free state was constructed at stationary operations of the engine. The residuals were then evaluated to classify the system health. Investigations were conducted for a conventional gasoline engine with port-fuel injection (PFI) and for a gasoline direct injection engine (GDI).

PL

W artykule zaproponowano metodę diagnozowania usterek w układzie dolotowym powietrza silnika benzynowego, nie wykrywanych przez system diagnostyki pokładowej samochodu. Celem jest detekcja i identyfikacja takich błędów jak zmiana charakterystyk czujników, błędy pomiaru przepływu powietrza lub nieszczelności w kolektorze dolotowym. Te usterki wpływają bezpośrednio na działanie układu dolotowego, co powoduje nierównomierność pracy silnika lub zmniejszenie jego mocy. Metoda polega na generowaniu pozostałości na podstawie różnic we wskazaniach czujników ciśnienia bezwzględnego w kolektorze i przepływomierza powietrza zainstalowanych w samochodzie oraz czujników opartych na modelu wirtualnym. Model empiryczny stanu bezawaryjnego został utworzony dla pracy silnika w stanie ustalonym. Pozostałości są następnie oceniane w celu sklasyfikowania stanu systemu. Badania przeprowadzono dla konwencjonalnego silnika benzynowego z wtryskiem wielopunktowym (PFI) i silnika benzynowego z wtryskiem bezpośrednim (GDI).

3

Fault detection using neuro-fuzzy networks

Kowal M., Korbicz J., Mendes M.J., Calado J.M.

Systems Science

|

2002

|

Vol. 28, nr 1

45-57

EN

Generally, there methodologies for developing and testing fault detection (FD) algorithms can be distinguished: software benches, hardware benches and industrial data. The current approach uses a hardware bench that consists of process under supervision (two interconnected stations), supervision unit, fault diagnosis unit and fault simulation unit. All elements of the bench are connected to a PROFIBUS network that acts as the communication system exchanging information between automation system and distributed field devices. A realistic and flexible environment for developing and testing FD systems has been constructed using elements commonly used in industry. During the current studies actuator faults, sensor faults and leakages have been considered as incipient and abrupt faults. The proposed FD algorithm is based on neuro-fuzzy models that are responsible for residual generation.

4

Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems

Patton R. J., Lopez-Toribio C. J., Uppal F. J.

International Journal of Applied Mathematics and Computer Science

|

1999

|

Vol. 9, no 3

471-518

EN

Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.