An integrated fault estimation and accommodation design for a class of complex networks

Cheng, Shuyao; Yang, Hao; Jiang, Bin

doi:10.1016/j.neucom.2016.08.043

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…In a fault-free situation, it is assumed that u i = 0, ∀i ∈ M C ; i.e., there is no external control affecting the system behavior. A fault diagnosis scheme of a network system is not the main focus of the present study, however, and is thus not considered herein; interested readers can refer to [8,9] for more detailed information. Theorem 1.…”

Section: Yang H Et Al Sci China Inf Scimentioning

confidence: 99%

Fault-tolerant control of energy-conserving networks

Yang

Zhang

et al. 2020

Sci. China Inf. Sci.

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Section: Yang H Et Al Sci China Inf Scimentioning

confidence: 99%

Fault-tolerant control of energy-conserving networks

Yang

Zhang

et al. 2020

Sci. China Inf. Sci.

Self Cite

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“…Consider the nonlinear systems described in (1) and fault estimation approach based on iterative learning scheme (13), and Assumption 5 holds. If the design parameters ( ) and ( ) satisfy the following inequality:…”

Section: Convergence Analysismentioning

confidence: 99%

“…Hence, it is further needed for the purpose of active fault-tolerant control to maintain the normal performance of systems. Over the past decades, observer-based fault estimation [12,13] has received considerable attention both in academic research and in industrial application domains. It has made significant progress and covered a wide scope of research issues such as linear systems [14], nonlinear systems [15], time-delay systems [16], uncertain systems [17], multiagent systems [18] and fuzzy systems [19].…”

Section: Introductionmentioning

confidence: 99%

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Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

et al. 2017

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An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions underconstraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.

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“…Fault diagnosis technology has always been an important topic of investigation and it is widely used in industrial process systems . In addition, with multidisciplinary development and the expansion of network theory, network‐based fault diagnosis methods, such as fault diagnosis based on Bayesian networks, fault diagnosis based on neural networks, and fault diagnosis based on complex networks, have also received increasing attention. Fault detection based on complex networks regards the whole industrial system as a network structure and the devices or monitoring variables in industrial systems are regarded as the nodes in the network.…”

Section: Introductionmentioning

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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An integrated fault estimation and accommodation design for a class of complex networks

Cited by 17 publications

References 38 publications

Fault-tolerant control of energy-conserving networks

Fault-tolerant control of energy-conserving networks

Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length

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

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