Fault Detection and Isolation System Based on Structural Analysis of an Industrial Seawater Reverse Osmosis Desalination Plant

Pérez-Zúñiga, Gustavo; Rivas-Pérez, Raul; Sotomayor-Moriano, Javier; Sánchez-Zurita, Victor

doi:10.3390/pr8091100

Cited by 15 publications

(8 citation statements)

References 33 publications

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“…It is assumed that there are two level sensors measuring the levels ℎ 1 and ℎ 2 in the two tanks. { y 1 = h 1 , y 2 = h 2 , measurement sensor of h 1 measurement sensor of h 2 (19) It is assumed that these two sensors are biased in their measurements, that is to say we consider two failures 1 and 2 affecting respectively the two DIAGNOSTYKA, Vol. 21, No.…”

Section: Wherementioning

confidence: 99%

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

Achbi¹,

Mhamdi²,

Kechida³

et al. 2020

Diagnostyka

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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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Section: Wherementioning

confidence: 99%

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

Achbi¹,

Mhamdi²,

Kechida³

et al. 2020

Diagnostyka

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show abstract

“…The major contribution of this research is the extension of the structural analysis approach [17,23,37] to propose a distributed FDI approach for the OPS, considering an optimization process in the selection of tests and the inclusion of neighbors subsystems at different levels to achieve the diagnostic goals. In order to guarantee maximum diagnosability and least exchange of sensor information between subsystems, local diagnosers should be efficiently built for each subsystem, in this sense, two algorithms for the design of local diagnosers are proposed, first only with local information and if necessary with progressive incorporation of information from neighboring subsystems to achieve detection and isolation of a set of faults of interest in OPS.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Detection and Isolation Approach for Oil Pipelines

et al. 2021

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Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated.

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“…Furthermore, it could also be used to perform fault prediction for improving the maintenance plan and to avoid undesired malfunctions. In the fault detection literature, a great number of methods have been dedicated to modelbased techniques [1,2] with great success in many cases, although a known drawback of such methods is their limitation to processes with relatively simple dynamics and sensiblity to model approximations. Examples of model-based methods are: the structural approach, which searches for irreducible relations that attains sensibility to the selected faults [3,4], and eigenstructure-based fault diagnosis, which deals with a minimization problem for the sensibility of perturbations against faults [5,6].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis via Neural Ordinary Differential Equations

2021

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Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data.

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Fault Detection and Isolation System Based on Structural Analysis of an Industrial Seawater Reverse Osmosis Desalination Plant

Cited by 15 publications

References 33 publications

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

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

Distributed Fault Detection and Isolation Approach for Oil Pipelines

Fault Diagnosis via Neural Ordinary Differential Equations

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