An Artificial Neural Network based expert system fitted with Genetic Algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal

Martínez-Martínez, Víctor; Gomez-Gil, Francisco Javier; Gómez-Gil, Jaime; Ruiz-Gonzalez, Ruben

doi:10.1016/j.eswa.2015.04.018

Cited by 35 publications

(16 citation statements)

References 31 publications

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“…Another important keyword is ANN, which has high frequencies of co-occurrence with "ES" and "AI", "prediction", "GA", "fuzzy logic", "SVM", and "CI", showing that ANN is the essential and important algorithm in AI research. By using new methods, in recent years, many scholars have been intent to improve ANN and enhance the efficiency of ANN by coupling with other algorithm, such as GA, and fuzzy logic [54][55][56][57][58][59]. In addition, the close relationship of AI with ANN and ES indicates that many scholars are dedicated to related research and published numerous articles.…”

Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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Abstract:In this article, we conducted the evaluation of artificial intelligence research from 1990-2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.

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Section: Abstract Hot Issues and Research Trend Analysismentioning

confidence: 99%

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Niu

Tang

et al. 2016

IJGI

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“…Como una muestra de la expansión de los sistemas expertos, se puede analizar el trabajo de Martínez et al [7], en el cual se proponen variadas técnicas que combinan el uso de sensores con redes neuronales como medios de predicción (con sus correspondientes métodos de aprendizaje), lo cual forma parte del sistema experto expuesto en ese trabajo. El presente artículo describe el SE Plástico, que constituye una modernización basada en una versión anterior.…”

Section: Introductionunclassified

Sistema experto para la identificación de plásticos

Brito

Rodríguez

2016

ing. Solidar

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Introducción: este artículo de investigación presenta una actualización del proyecto “Sistema Experto para la selección de materiales plásticos para el diseño y la elaboración de nuevos productos” desarrollada durante 1990 en la Universidad Central “Marta Abreu” de Las Villas. El objetivo de la investigación actual, realizada durante el 2015 en la Universidad Cooperativa de Colombia, es actualizar el sistema experto desarrollado en 1990. Metodología: para el desarrollo del sistema experto, se usó la metodología Common kads, la cual permite estructurar el conocimiento al hacer una distinción entre el dominio de la aplicación, la tarea y la inferencia, lo que facilita la reutilización de componentes. En el proceso de inferencia, se usa la dirección de búsqueda dirigida por objetivos, debido a las características del conocimiento contenido en las bases, en las cuales el proceso de inferencia comienza sin la existencia de datos preliminares. Resultados: la nueva versión del sistema mejora las inferencias, ofrece una interfaz más amigable y amplía el conocimiento. Conclusiones: la ampliación de las bases de conocimiento, adaptadas a nuevas situaciones, incide en la obtención de respuestas más fundamentadas y mejores; la implementación del nuevo sistema sobre ucShell, versión 3.0 proporciona una mejoría notable en la interfaz, que incluye la posibilidad de ofrecer imágenes y videos durante el proceso de inferencia y admite bases de conocimientos de cualquier tamaño y la facilidad de cambiarse entre ellas.

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“…This method can be applied to predict the desired output parameters when the database of the problem represents all relationships. ANN have been used in different engineering applications such as mechanical vibrations (Koide et al, 2014;Lagaros and Papadrakakis, 2012;Liu et al, 2015;Martínez-Martínez et al, 2015;Perez-Ramirez et al, 2016) rail rolling processing (Altınkaya et al, 2014), creep modelling (Düğenci et al, 2015), steel projectile penetration depth (Hosseini and Dalvand, 2014) and internal combustion engines to estimate some important parameters of fuels on emissions (Cay, 2013;Czarnigowski, 2010). The uses of ANN in the field of defence systems have recently begun to increase.…”

Section: Introductionmentioning

confidence: 99%

Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

Koç

Esen

Çay

2016

Lat. Am. j. solids struct.

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For realistic applications, design and control engineers have limited modelling options in dealing with some vibration problems that hold many nonlinearity such as non-uniform geometry, variable velocity loadings, indefinite damping cases, etc. For these reasons numerous time consuming experimental studies at high costs must be done for determining the actual behaviour such nonlinear systems. However, using advantages of multiple computational methods like Finite Element Method (FEM) together with an Artificial Intelligence (ANN), many complicated engineering problems can be handled and solved to some extent. This study, proposes a new collective method to deal with the nonlinear vibrations of the barrels in order to fulfil accurate shooting expectancy. Using known analytical methods, in practical, to determine dynamic behaviour of the barrel beam is not possible for all conditions of firing that include numerous varieties of ammunition for different purposes, and each projectile of different ammunition has different mass and exit velocity. In order to cover all cases this study proposes a new method that combines a precise FEM with ANN, and can be used for determining the exact dynamic behaviour of a barrel for some cases and then for precisely predicting the behaviour for all other possible cases of firing. In this study, the whole nonlinear behaviour of an antiaircraft barrel were obtained with 3.5% accuracy errors by ANN trained by FEM using calculated analysis results of ammunitions for a particular range. The proposed FEM-ANN combined method can be very useful for design and control engineers in design and control of barrels in order to compensate the effect of nonlinear vibrations of a barrel for achieving a higher shooting accuracy; and can reduce high-cost experimental works.

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An Artificial Neural Network based expert system fitted with Genetic Algorithms for detecting the status of several rotary components in agro-industrial machines using a single vibration signal

Cited by 35 publications

References 31 publications

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis

Sistema experto para la identificación de plásticos

Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

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