A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer With Artificial Immune System as an Online Approximator

Thumati, Balaje T.; Halligan, Gary R.; Jagannathan, S.

doi:10.1109/tcst.2012.2186635

Cited by 26 publications

(9 citation statements)

References 13 publications

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“…16 In some cases, AIS have been associated with fault forecasts and predictions 17,18 or have supported the optimisation of control strategies 9,19,20 System target AIS have been studied in various types of systems, including software, electronic and electric equipment 16,21,22 and mechanical components. 15,18,23,24 Recently, the approach has been applied to developing the maintenance of energy systems and smart grids [25][26][27]…”

Section: Research Domain Descriptionmentioning

confidence: 99%

“…Research studies underline that AIS can be easily scalable and thus can be applied without any constraints to a single part or equipment 17,18,23,29,36 rather than to a plant area 28,37,38…”

Section: Coverage Areamentioning

confidence: 99%

“…The nascent nature of the topic in industry reflects the maturity level of AIS research. The majority of existing works are conceptual and present a theoretical point of view, which in some cases was tested through simulations; however, few research studies have reached a prototype level, 14,23,28,32 while no work has reported an empirical application thus far AIS: artificial immune systems; MAS: multi-agent systems.…”

Section: Self-capabilitiesmentioning

confidence: 99%

See 2 more Smart Citations

An artificial immune intelligent maintenance system for distributed industrial environments

Fasanotti¹,

Cavalieri

Dovere

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Maintenance services of geographically dispersed industrial applications, such as oil transfer systems via pipelines and wastewater treatment plants, are affected by high logistics costs and risks of permanent downtimes. The increasing availability of smart technologies and devices has led to the introduction of advanced prognostic and diagnostic systems to support maintenance activities. In this context, artificial immune systems support the development of industrial applications, where machines and equipment are capable of self-repairing, healing and learning due to their ability to learn from experience. However, the applicability of artificial immune systems has a limited set of contexts along with a low incidence of real-word implementations in the literature, and thus, additional explorative studies are necessary. This article describes a proposed hybrid system conceived by integrating a multi-agent system-based architecture with the main features of artificial immune systems and evaluates its potential applications in two different industrial settings. The flexibility of the behaviour of artificial immune systems methodologies allows for the implementation of a reliable diagnostic and prognostic system, while the choice of multi-agent system architecture enables a mix of autonomy and distributed processing that overcomes the strong limitations of a reduced training dataset.

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Section: Research Domain Descriptionmentioning

confidence: 99%

Section: Coverage Areamentioning

confidence: 99%

Section: Self-capabilitiesmentioning

confidence: 99%

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An artificial immune intelligent maintenance system for distributed industrial environments

Fasanotti¹,

Cavalieri

Dovere

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…14 In state monitoring and fault prediction, support vector probability density estimation was proposed to predict fault in a complex system. 15 In Thumati et al, 16 fault diagnosis and prediction method was developed based on an adaptive observer, where artificial immune system was applied to approximate the unknown function.…”

Section: Introductionmentioning

confidence: 99%

Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

Liu

Zhang

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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When adopting the conventional grey model (GM(1,1)) to predict weak thruster fault for autonomous underwater vehicles, the prediction error is not always satisfactory. In order to solve the problem, this article develops a new weak thruster fault prediction method based on an improved GM(1,1). In the developed GM(1,1) based fault prediction method, this article mainly makes improvement in the following aspects: construction of grey background value, solution of whiting differential equation and construction of predicted sequence. Specifically, the integral operation is used in range of the two adjacent steps to obtain the grey background value at first. Second, in the solving of whiting differential equation, the point corresponding to the least difference between the accumulated generation sequence and its predicted sequence is determined, and then this special point’s value in the original sequence is considered as the initial condition of the whiting differential equation. Third, in the construction of predicted sequence, another predicted value is obtained based on the error sequence between the accumulated generating operation sequence and its predicted sequence, and then the new predicted result is used to re-adjust the accumulated generating operation sequence, so as to guarantee the re-adjustability of the fault prediction result. Finally, experiments are performed on Beaver 2 autonomous underwater vehicle to evaluate the prediction performance of the developed method.

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“…In general, additive faults are modeled as an unknown input to the system or process. Several approaches have been proposed to solve the fault detection and diagnosis problem for nonlinear systems subjected to additive faults [1,[8][9][10][11]. Component faults and some actuator and sensor faults appear in the form of multiplicative faults.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fault detection and estimation scheme for a class of uncertain nonlinear systems

2014

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This paper presents a novel fault detection and estimation (FDE) scheme for a class of Lipschitz nonlinear systems subjected to modeling and measurement uncertainties. Many works in this field assume that the states of the system are measurable and the fault function acts as an additive term. The proposed FDE strategy deals with the uncertain nonlinear systems subjected to multiplicative faults, and it does not rely on availability of the full state. Multiplicative fault corresponds to the parameter or structure changes in the system or in the process model. Also, actuator gain fault is another important fault type which can be modeled as a multiplicative fault. The effects of this kind of fault are combined with the inputs and outputs of the system in a multiplicative form which, in turn, make the detection and estimation of the fault complex. The proposed scheme is based on an adaptive diagnostic observer that not only can estimate the states of the system and generate the residual signal simultaneously, but also is able to estimate the characteristic and magnitude of an unknown fault. In the fault detection step, the threshold is derived analytically to ensure the robustness of the proposed detection scheme against uncertainties. Also in the fault estimation step, a robust adaptive law based on the switching σ -modification is developed to estimate the detected fault accurately. Design steps of the proposed estimation scheme are introduced in the form of LMI problem. This formulation provides an effective way to calculate the design parameters. The proposed FDE scheme guarantees that all signals are uniformly ultimately bounded. Simulation results of a single-link, flexible joint, robotic arm show the effectiveness and robustness of the proposed FDE strategy.

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A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer With Artificial Immune System as an Online Approximator

Cited by 26 publications

References 13 publications

An artificial immune intelligent maintenance system for distributed industrial environments

An artificial immune intelligent maintenance system for distributed industrial environments

Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

Adaptive fault detection and estimation scheme for a class of uncertain nonlinear systems

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