Methodology for monitoring and diagnosing faults of hybrid dynamic systems: a case study on a desalination plant
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Laboratoire d’Automatique et Informatique de Guelma (LAIG lab.),
Université 8 Mai 1945 Guelma, BP 401, Guelma 24000, Algérie
Submission date: 2019-08-24
Final revision date: 2019-12-14
Acceptance date: 2020-01-03
Online publication date: 2020-01-07
Publication date: 2020-01-07
Corresponding author
Achbi Mohammed Said   

Laboratoire d’Automatique et Informatique de Guelma (LAIG lab.),
Diagnostyka 2020;21(1):27-33
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.
Alur R, Courcoubetis C, Halbwachs N, Henzinger T, Ho PH, Nicollin X, Olivero A, Sifakis J, Yovine S. The Algorithmic Analysis of Hybrid Systems Theoretical Computer Science. 1995; 138: 3–34.
Jang JS. ANFIS: Adaptive-Network-based Fuzzy Inference System. IEEE Transctions on Systems, Man and Cybernetics. 1993; 23:665-685.
Sayed-Mouchaweh M. ed. Fault diagnosis of hybrid dynamic and complex systems. Springer International Publishing. 2018.
Antsaklis, Panos J, James A, Stiver JA, Lemmon M. Hybrid system modeling and autonomous control systems In Hybrid systems, Springer. Berlin. Heidelberg.1992: 366-392.
Cocquempot V, El Mezyani T, Staroswiecki M. Fault detection and isolation for hybrid systems using structured parity residuals. IEEE In 5th Asian Control Conference. 2004; 2: 1204-1212.
Maler O, Manna Z, Pnueli A. From Timed to Hybrid Systems; In de Bakker J W, Huizing C, de Roever W P, and Rozenberg G. editors, Real-Time: Theory in Practice, 600, Springer-Verlag. 1991; 447–484.
Achbi MS, Kechida S. Fault diagnosis of a reverse osmosis water desalination plant through a hybrid approach. International conference on Electronics and new technologies. ICENT. M’sila. 2017.
Blanke M, Kinnaert M, Lunze J, Staroswiecki M, Schröder J. Diagnosis and fault-tolerant control. 2006. Berlin: Springer.
Chine W, Mellit A, Lughi V, Malek A, Sulligoi G, Pavan AM. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renew. Energy. 2016; 90:501–512.
Mahmoud M S. Fuzzy Control, Estimation and Diagnosis. Saudi Arabia: Springer International. 2018.
Daher. Default diagnosis and prognosis for a preventive and predictive maintenance. Application to a distillation column. 2018. PhD diss.
Achbi MS, Kechida S. Fault tolerant control of Reverse Osmosis Desalination Plant with the application of SCADA system. 2nd international conference on Applied Automation and Industrial Diagnostics. ICAAID. Djelfa. 2017.
Champagnat R, Esteban P, Pingaud H, Valette R. Modeling and simulation of a hybrid system through Pr/Tr PN-DAE model. In ADPM. 1998; 98(3):131-137.
Borutzky W. Bond graph model-based fault diagnosis of hybrid systems. Switzerland: Springer International Publishing. 2014.
Achbi MS, Kechida S. Hybrid dynamic systems fault diagnosis approach based on hybrid automata and ANFIS. 2nd international conference on Applied Automation and Industrial Diagnostics. ICAAID. Djelfa. 2017.
Maaref B, Abazi ZS, Dhouibi H, Messaoud H, and Gascard E. Mixed approach for fault diagnosis and fault location of hybrid systems. IFAC-PapersOnLine. 2016; 49(12):1002-1007.
Vento J, Travé-Massuyès L, Puig V, Sarrate R. An incremental hybrid system diagnoser automaton enhanced by discernibility properties. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2014; 45(5): 788-804.
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