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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

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2021

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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.

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Methodology to knowledge discovery for fault diagnosis of hybrid dynamical systems: demonstration on two tanks system

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

Diagnostyka

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2020

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Vol. 21, No. 4

115--122

EN

The work carried out in this article concerns on the implementation off a diagnostic procedure for hybrid dynamic systems (HDS) whose objective is to guarantee the proper functioning of industrial installations. In this context, the main contributions of this work are summarized into three parts: The first part is oriented to the modeling approach dedicated to HDS. The aim is to find an adequate model combining both aspects (continuous and discrete dynamics). The use of Neuro-fuzzy networks makes it possible to build a model of the system and to follow all the modes without it being necessary to identify or discern them. The second part concerns the synthesis of a fault diagnostic technique based on a fuzzy inference system. A Neuro-Fuzzy network based is used for residual generation, while for the residual evaluation, a fuzzy reasoning model is used which can mainly introduce heuristic information into the analysis scheme and takes the appropriate decision regarding the actual behaviour of the process. The proposed approach is successfully applied to monitoring faults of a non-linear three-tank system and the results confirm the effectiveness of this approach.

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Methodology for monitoring and diagnosing faults of hybrid dynamic systems: a case study on a desalination plant

Achbi Mohammed Said, Kechida Sihem

Diagnostyka

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2020

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Vol. 21, No. 1

27--33

EN

The imperative of quality and productivity has increased the complexity of technological processes, posing the problem of reliability. Today, fault diagnosis remains a very important task because of its essential role in improving reliability, but also in minimizing the harmful consequences that can be catastrophic for the safety of equipment and people. Indeed, an effective diagnosis not only improves reliability, but also reduces maintenance costs. Systems in which dynamic behaviour evolves as a function of the interaction between continuous dynamics and discrete dynamics, present in the system, are called hybrid systems. The goal is to develop monitoring and diagnostic procedures to the highest level of control to ensure safety, reliability and availability objectives. This article presents an approach to the diagnosis of hybrid systems using hybrid automata and neural-fuzzy system. The use of the neural-fuzzy system allows modeling the continuous behaviour of the system. On the other hand, the hybrid automata gives a perfect estimate of the discrete events and make it possible to execute a fault detection algorithm mainly consists of classifying the appeared defects. On the implementation plan, the results were applied in a water desalination plant.

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Diagnosis of hybrid systems through bond graph, observers and timed automata

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

Diagnostyka

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2020

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Vol. 21, No. 3

113--125

EN

Despite technological advances and progress in industrial systems, the fault diagnosis of a system remains a very important task. In fact an effective diagnosis contributes not only to improved reliability but also to a decrease in maintenance costs. This paper presents an approach to a diagnosis of hybrid systems thanks to the use of Bond Graphs, Observer and Timed Automata. Dynamic models (in normal and failing mode) are generated by an observer based methods as well as through state equations generated by the Bond Graphs model. The procedure of fault localization through a method based on the observer does not allow locating faults with the same signature of failure. Thus the diagnosis technique for the localization of these defects will be based on the time analysis using Timed Automata. The proposed approach is then validated by simulation tests in a two tanks hydraulic system.