Model-based approach for fault diagnosis using set-membership formulation

Chatti, Nizar; Guyonneau, Rémy; Hardouin, Laurent; Verron, Sylvain; Lagrange, Sébastien

doi:10.1016/j.engappai.2016.08.001

Cited by 16 publications

(9 citation statements)

References 28 publications

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“…In the work by Djeziri et al, 55 parameter uncertainties were added to the BG model, using the linear fractional transformation (LFT) configuration and leading to robust thresholds. Notice that another way to address model uncertainties was given in the work by Chatti et al 47 and considers that unknown uncertainties are within known bounds, using an interval consistency technique.…”

Section: Discussionmentioning

confidence: 99%

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Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Tidriri

Chatti

Verron

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Fault detection and diagnosis for industrial systems has been an important field of research during the past years. Among these systems, the Tennessee Eastman process is extensively used as a realistic benchmark to test and compare different fault detection and diagnosis strategies. In this context, data-driven approach has been widely applied for fault detection and diagnosis of the Tennessee Eastman process, by exploiting the massive amount of available measurement data. However, only few published works had attempted to deal with the dynamic behavior of the whole system including the mixing zone, circulating pumps, the reactor, the separator, the stripper, and so on, because of the difficulty of modeling physical phenomena that may occur in such complex system. In this article, an accurate model of the Tennessee Eastman process, properly tailored for fault detection and diagnosis purposes, is provided. This model shows better fault detection and diagnosis performances than all the others proposed in the literature and gives better or comparable results with the data-driven approaches. This work uses the bond graph methodology to systematically develop computational and graphical model. This methodology provides a physical understanding of the system and a description of its dynamic behavior. The bond graph model is then used for monitoring purposes by generating formal fault indicators, called residuals, and algorithms for fault detection and diagnosis. Hence, abnormal situations are detected by supervising the residuals' evolution and faults are isolated using the nature of the violated residuals. Therefore, the dynamic model of the Tennessee Eastman process can now be used as a basis to achieve accurately different analysis through the causal and structural features of the bond graph tool.

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Section: Discussionmentioning

confidence: 99%

“…16,45,46 BG for FDD BG has been successfully applied for FDD purposes. 15,16,23,46,47 It enables to generate fault indicators, called ARRs.…”

Section: Bg For Modelingmentioning

confidence: 99%

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Tidriri

Chatti

Verron

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

Self Cite

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“…The current fault diagnosis methods are numerous and have been built on one another effectively. In general, fault diagnosis methods are mainly divided into the following parts: methods based on analytical models; methods based on qualitative knowledge; and methods based on data . Initially, fault diagnosis research is mostly based on an analytical model and, consequently, many achievements in the field include state estimation methods, parameter estimation methods, and equivalent space methods .…”

Section: Related Workmentioning

confidence: 99%

A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes

Geng

Wang

et al. 2018

Can J Chem Eng

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With the trend towards large-scale structure and complexity in modern chemical processes, it is difficult to describe the system operating status and fault conditions via traditional approaches. In addition, there are obstacles to fault detection research in complex chemical processes due to the characteristics of the equipment hardware used in chemical systems, data collection and handling, strong internal correlations in chemical systems, factors affecting transmission, and randomness and cascade in process failures. Therefore, this paper presents a fault detection method based on horizontal visibility graph (HVG) analysis-integrated complex networks. The data for each variable in the system are regarded as a time series, each time series is modelled into a network by the horizontal visibility algorithm, and each single-layer network corresponding to a time series is abstracted as a node. Meanwhile, the correlation between two single-layer networks is used to characterize the correlation between the corresponding nodes. Moreover, a complex network structure representing the chemical system can be constructed from the correlations. In addition, according to the correlation ratio matrix obtained from the fault state and the normal state, the variance of each node determines the faulty node. Finally, we verify the validity and the effectiveness of the proposed method by applying it to the Tennessee Eastman (TE) process.

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“…The orthotopic method [13, 14], because it guarantees estimation accuracy in the case of low computational complexity, has been widely used. As the set membership algorithm only requires that system noise be bounded and the boundary is a priori known, it can be adopted in most systems and thus is widely applied to fields such as fault diagnosis [15–17]. However, the current fault diagnosis methods based on set membership filtering mostly address fault detection.…”

Section: Introductionmentioning

confidence: 99%

Orthotopic‐filtering‐based hierarchical fault diagnosis algorithm for linear recursive models

Wang

et al. 2020

IET Control Theory & Appl

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Model-based approach for fault diagnosis using set-membership formulation

Cited by 16 publications

References 28 publications

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process

A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes

Orthotopic‐filtering‐based hierarchical fault diagnosis algorithm for linear recursive models

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