Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

Majdani, Farzan; Petrovski, Andrei; Doolan, Daniel C.

doi:10.1007/s12530-017-9206-8

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…With the increased applications of AI in smart cities design, the role of AI is increasingly dominating in smart grids, intelligent transport systems, and autonomous vehicles [5]. For example, evolving ANN-based sensors are already used for processing, detecting anomalies, and making predictions in context-aware cyber physical systems [6]. Cyber-attack predictions have been produced by generating attack graphs and predicting future attacks, and have proven to be both practicable and effective [7].…”

Section: Current State-of-the-artmentioning

confidence: 99%

Super-forecasting the ‘technological singularity’ risks from artificial intelligence

Radanliev

Roure

Maple³

et al. 2023

Preprint

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This article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from AI itself. In other words, the research concern in this article is not just with securing a system, but to analyse how the system responds when (internally and externally caused) failure and compromise occur. This is an important methodological principle because not all systems can be secured, and we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of artificial intelligence in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodology that uses AI to defend from cyber risk. The methodology is focused on addressing the risk of AI attack, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

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Section: Current State-of-the-artmentioning

confidence: 99%

Super-forecasting the ‘technological singularity’ risks from artificial intelligence

Radanliev

Roure

Maple³

et al. 2023

Preprint

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“…The conclusion of the study was that the system reliability of the repairable components can be analysed in a more flexible and intelligent manner. Within the domain of offshore assets' integrity assurance, Reference [57] proposed inferential sensors to predict the status of the machine. They presented high-accuracy predictions of gas turbines' status up to 15 h in advance.…”

Section: Health Statusmentioning

confidence: 99%

“…Yu Xhang et al [46] 2013 VBGMM Determine operational change between steadystate and transients Manjit Verma and Amit Kumar [56] 2014 PN Qualitative and quantitative analysis for modelling and analysing machine behaviour Hamid Asgari et al [49] 2016 NARX Gas turbine's start-up dynamics' assessment Cristiano Hora Fontes and Otacílio Pereira [50] 2016 SPCA + Fuzzy C-means Operational pattern recognition Nallamilli P. G. Bhavani et al [53] 2016 ANN Intelligent sensor to monitor and control combustion quality Pogorelov G.I et al [43] 2017 RNN Dynamic performance assessment Abshukirov Zhandos and Jian Guo [47] 2017 Decision tree Behaviour analysis based on temperature evolution Josué Enríquez-Zárate et al [51] 2017 GSGP Model parameters' estimation Maria Grazia De Giorgi et al [44] 2018 ANN, SVM Model comparison for health status monitoring Jiao Liu et al [52] 2018 CNN Abnormal operation detection Farzan Majdani et al [57] 2018 ANN Inferential sensor for machine status assessment K. Sujatha, G. et al [54] 2019 ANN + PSO Exhaust gas estimation Hossein Shahabadi Farahani et al [45] 2020 TL Improvement of health monitoring system Yanghui Tan et al [48] 2020 OCSVM, SVDD, GKNN, LOF, IForest, and ABOD…”

Section: Reference Year ML Model Applicationmentioning

confidence: 99%

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

2021

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Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

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“…Atualmente, os sistemas inteligentes evolutivos são um dos principais temas pesquisados na modelagem de da-dos online (Kasabov et al, 2017). Diversos trabalhos que envolvam sistemas evolutivos, ou seja, sistemas que sejam capazes de alterar e atualizar suas estruturas quando necessário a partir de um fluxo de dados, foram desenvolvidos nosúltimos anos (Majdani et al, 2018(Majdani et al, ) (Škrjanc et al, 2019. Este tipo de abordagemé adequada para lidar com situações em que nãoé possível prever algum acontecimento.…”

Section: Introductionunclassified

Controle Granular Fuzzy Evolutivo Aplicado à Navegação de Veículos Inteligentes

Tavares

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2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Noúltimos anos, diversos avanços em sistemas de condução inteligentes têm sido apresentados no mundo. Um dos desdobramentos de tais avançosé a utilização de veículos inteligentes a fim de trazer mais eficiência nos transportes, melhorar a mobilidade nos grandes centros, e aumentar a segurança dos condutores e pedestres em uma via. No entanto, diversos desafios de caráter técnico e não-técnico ainda devem ser vencidos para que os veículos possam atingir níveis de autonomia elevados e se tornarem populares nas ruas. Um desses desafiosé garantir sua condução de forma segura e eficiente. Este trabalho tem como objetivo validar a utilização de uma abordagem de controle de navegação inteligente baseada em modelo fuzzy evolutivo de veículos inteligentes a partir do fluxo online de dados provenientes dos sensores embarcados no veículo. A modelagem e controle apresentados se mostraram satisfatórios para diversos casos.

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Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

Abstract: applied across a wide range of problem domains requiring processing, analysis and interpretation of data obtained from heterogeneous resources.

Cited by 10 publications

References 18 publications

Super-forecasting the ‘technological singularity’ risks from artificial intelligence

Super-forecasting the ‘technological singularity’ risks from artificial intelligence

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

Controle Granular Fuzzy Evolutivo Aplicado à Navegação de Veículos Inteligentes

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