Neural Network-Based State Estimation Schemes

Talebi, Heidar Ali; Abdollahi, Farzaneh; Patel, Rajni V.; Khorasani, Khashayar

doi:10.1007/978-1-4419-1438-5_2

Cited by 3 publications

(2 citation statements)

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“…Problems of predictive maintenance usage are presented in several publications. They often consider the use of artificial neural networks [4][5][6][7][8][9][10][11], fuzzy logic [12][13][14][15], genetic algorithms [16][17][18], or expert systems [19][20][21][22].…”

Section: State Of the Artmentioning

confidence: 99%

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Kuźnar,

Lorenc

2023

Applied Sciences

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In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions and a state of failure may increase the reliability of the entire supply chain. From the aspect of sustainability, the issue of reducing the number of failures also makes it possible to reduce supply chain disturbances, to reduce costs associated with delays, and to reduce the materials needed for the repair of the means of transport, since, in this case, the costs only relate to the replaced elements before their damage. Thus, it is impossible for more serious damage to occur. Often, failure of one item causes damage to others, which generates unnecessary costs and increases the amount of waste due to the number of damaged items. This article provides an author’s method of technical condition prediction; by applying the method, it would be possible to develop recommended maintenance activities for key elements related to the safety and reliability of transport. The combination of at least two artificial intelligence methods allows us to achieve very good prediction results thanks to the possibility of individual adjustments of weights between the methods used. Such predictive maintenance methods can be successfully used to ensure sustainable development in supply chains.

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Section: State Of the Artmentioning

confidence: 99%

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Kuźnar,

Lorenc

2023

Applied Sciences

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“…Moreover, the identification of the static inverse neural network model is not efficient in case of parameter variations. (Talebi et al, 2010) and (Lakhal et al, 2010) have developed an adaptive observer based on backpropagation which is a well-known neural network learning algorithm (Werbos, 1974). An idea could be to combine this method with a disturbance observer.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Neural Network Prediction against Unknown Disturbances with Neural Network Disturbance Observer

Pouilly-Cathelain

Feyel

Duc

et al. 2019

Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics

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Neural network prediction is a very challenging subject in the presence of disturbances. The difficulty comes from the lack of knowledge about perturbation. Most papers related to prediction often omit disturbances but, in a natural environment, a system is often subject to disturbances which could be external perturbations or also small internal parameters variations caused, for instance, by the ageing of the system. The aim of this paper is to realize a neural network predictor of a nonlinear system; for the predictor to be effective in the presence of varying perturbations, we provide a neural network observer in order to reconstruct the disturbance and compensate it, without any a priori knowledge. Once the disturbance is compensated, it is easier to realize such a global neural network predictor. To reach this goal we model the system with a State-Space Neural Network and use this model, completed with a disturbance model, in an Extended Kalman Filter.

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Intelligent Techniques for Control and Fault Diagnosis in Pressurized Water Reactor: A Review

Kumar,

Devakumar

2024

Earth Sciences and Human Constructions

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Nuclear reactors serve approximately 10% of the world’s energy usage, and over 430 Nuclear Power Plants (NPP) are currently built globally. They are safety-critical systems as neutron flux density in the nuclear reactor core has to be critically controlled within limits. The parameters of a reactor core should be monitored and optimally regulated to increase the performance of the system. Also, any fault in an NPP system may potentially compromise plant safety. Thus, implementing early Fault Detection and Diagnosis (FDD) techniques becomes crucial. With considerable advancements in computational speed and electronics becoming costeffective, Artificial Intelligence (AI) has grown implausible in recent times. This review article discusses on few AI techniques to optimally control the neutron flux density and design an effective fault diagnosis algorithm to detect sensor faults in the nuclear reactor core.

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Neural Network-Based State Estimation Schemes

Cited by 3 publications

References 0 publications

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Enhancing Neural Network Prediction against Unknown Disturbances with Neural Network Disturbance Observer

Intelligent Techniques for Control and Fault Diagnosis in Pressurized Water Reactor: A Review

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