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Artificial intelligence for predictive maintenance in the railcar learning factories

Ilesanmi Daniyan, Khumbulani Mpofu, Moses Oyesola, Boitumelo Ramatsetse, Adefemi Adeodu
2020 Procedia Manufacturing  
The learning factories are platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice.  ...  Abstract The learning factories are platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice  ...  Having mentioned the merits of the AI system in the railcar industry, there is a need to bridge the gap between the learning and practice of the AI system through the learning factories.  ... 
doi:10.1016/j.promfg.2020.04.032 fatcat:j7o4lgcnmbf43gz55bd5y4k4fe

Learning in the artificial factory

M. Natter, M. Feurstein, A. Mild, A. Taudes, M. Trcka, G. Dorffner, C. Merz
Proceedings of the 33rd Annual Hawaii International Conference on System Sciences  
We study the effects of various incentive schemes on the learning behavior of teams in an artificial factory.  ...  Modeling the new product development process, we demonstrate, how production and marketing agents learn to coordinate their actions in order to produce the optimal product with respect to their incentive  ...  In contrast to individual learning, we define this process as organizational learning [7] or outer loop learning.  ... 
doi:10.1109/hicss.2000.926646 dblp:conf/hicss/NatterFMTTDM00 fatcat:ehdgm5ju6jeubej7tkimyrk5ue

A Simulation Study on an Artificial Neural Network based Automatic Control System of a Plant Factory

Kwang-Kyu Seo
2013 International Journal of Control and Automation  
The simulation results show the application possibility of the proposed system in a plant factory.  ...  This paper proposes a framework to design an automatic control system of a plant factory based on artificial neural network (ANN) and presents the simulation results of the proposed system.  ...  Acknowledgements This work was supported by research funds of Sangmyung University in 2013.  ... 
doi:10.14257/ijca.2013.6.5.12 fatcat:3bdskzl5sfd33o4nv6yyhivt7u

Envisioning future innovative experimental ecosystems through the foresight approach. Case: Design Factory

Vikram Munigala, Päivi Oinonen, Kalevi Ekman
2017 European Journal of Futures Research  
We analyze how the ways of working, spaces, and teaching methods of one such ecosystem, Design Factory at Aalto University in Finland, could support students learning in the year 20 × 6 {x = 2, 3}.  ...  The results from the study are six future scenarios for the Design Factory, that have implications for innovation ecosystems in general.  ...  Acknowledgements We would like to thank to all of the people who have actively participated in the study.  ... 
doi:10.1007/s40309-017-0128-2 fatcat:gldktcz44fe6tivhx62pffjbra

A Web-based Intelligent Virtual Learning Environment for Industrial Continuous Improvement

Xuesong Chi, Trevor Spedding
2006 2006 IEEE International Conference on Industrial Informatics  
Artificial intelligence techniques has been used for many years in educational systems, however the presentation of domain knowledge is one of the most concerning issues which stems the evolvement of the  ...  Artificial intelligence techniques has been used for many years in educational systems, however the presentation of domain knowledge is one of the most concerning issues which stems the evolvement of the  ...  The authors would like to express their appreciations to the Royal Statistical Society Centre for Statistical Education, the Maths, Stats and OR network, UK, the Medway School of Engineering, University  ... 
doi:10.1109/indin.2006.275771 fatcat:d7lvdyqoknff7gb4eyxqmftnei

Artificial Intelligence Technology analysis using Artificial Intelligence patent through Deep Learning model and vector space model [article]

Yongmin Yoo, Dongjin Lim, Kyungsun Kim
2021 arXiv   pre-print
Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society.  ...  For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level.  ...  Although domains are different for each factory, this indicates that domain knowledge is very important in using AI in the factory industry.  ... 
arXiv:2111.11295v1 fatcat:4fjqnr2lufgl7d2afrlahyoiqm

Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

Hamed Nazerian, Adel Shirazy, Aref Shirazi, Ardeshir Hezarkhani
2022 International Journal of Science and Engineering Applications  
These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.  ...  Artificial neural network (ANN) is one of the practical methods for prediction in various sciences.  ...  in the world, as well as the presence of young, outstanding, and experienced people in different factory units.  ... 
doi:10.7753/ijsea1102.1001 fatcat:ito4pbaz4feeba4qfjrbubn7hu

Intelligent Robotics in Smart-Factories during the Fourth Industrial Revolution

Avishay David, Pavlov Veselin, Pavlova Galia, Kashi Guy
2019 International Journal of Robotic Engineering  
The application of intelligent and colubrious robots has its own place in evolved industrialization. These are smart factories and plants.  ...  and self-learning.  ...  When the network secure the action of the system, the main functions will be held by self-learning robots, using artificial intelligence.  ... 
doi:10.35840/2631-5106/4117 fatcat:utqz673sxrcthii2k65rnrcrle

A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry

Jessica Walther, Matthias Weigold
2021 Energies  
In the context of the European Green Deal, the manufacturing industry faces environmental challenges due to its high demand for electrical energy.  ...  Thus, measures for improving the energy efficiency or flexibility are applied to address this problem in the manufacturing industry.  ...  Artificial Intelligence Machine Learning Deep Learning Modelling Focus Two categories can be distinguished, on which the studies in the field of energy modelling are focused.  ... 
doi:10.3390/en14040968 fatcat:yawpatncdjfy7lqpgouwbtwvce

Intelligent Information Systems for Knowledge Work(ers) [chapter]

Klaus-Dieter Althoff, Björn Decker, Alexandre Hanft, Jens Mänz, Régis Newo, Markus Nick, Jörg Rech, Martin Schaaf
2006 Lecture Notes in Computer Science  
A "deep" integration of case-based reasoning and experience factory is a first successful step in this direction [Tau00,Nic05].  ...  In addition, the cost-benefit analysis from the service provider point of view needs to be positive.  ...  Already before the invention of the experience factory approach, and until the mid 1990s also independent from it, case-based reasoning was introduced in the area of cognitive science and artificial intelligence  ... 
doi:10.1007/11790853_42 fatcat:aypurbj73vdtdma3g2k4o7uuce

Digital Transformation of Air Compressor Utilization Based on AI, ML in Smart Factory Management

Milan Kumar et al., Milan Kumar et al.,, TJPRC
2020 International Journal of Mechanical and Production Engineering Research and Development  
The development of artificial intelligence with machine learning technologies and their benefits are firstly discussed.  ...  Subsequently, computational methods supported machine learning are presented specially aim to improve system performance in manufacturing.  ...  As a breakthrough in artificial intelligence, deep learning demonstrates outstanding performance in various applications of speech recognition, image recondition, natural language processing, multimodal  ... 
doi:10.24247/ijmperdjun2020877 fatcat:x67hkposbvcbjd4knpqinen5qu

Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Sehrish Malik, DoHyeun Kim
2021 Actuators  
The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources.  ...  The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer's experience based on learnings from past trends.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/act10020027 fatcat:ddbun34q7zejhhmm4ufkm275gm

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu, Mădălina Cocoșatu
2021 Electronics  
and flexibility, configuring the smart factory.  ...  upon the progression of operations advancing a system to the intended state in CPPSs.  ...  on artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, cognitive automation, and deep learning-assisted smart process management in smart factory performance  ... 
doi:10.3390/electronics10202497 fatcat:rryhw72fhvalloix23qkxwh4ca

SSA-SL Transformer for Bearing Fault Diagnosis under Noisy Factory Environments

Seoyeong Lee, Jongpil Jeong
2022 Electronics  
In addition, previous studies assumed the factories situated in the bearing defect research were insufficient.  ...  Therefore, a recent research was conducted that applied an artificial intelligence model and the factory environment.  ...  Data Availability Statement: The data used in the paper can be found at https://engineering.case. edu/bearingdatacenter/download-data-file.  ... 
doi:10.3390/electronics11091504 fatcat:ujmmdyyinvbbxoe46qxsfpe3im

Artificial Neural Network Models for Predicting the Energy Consumption of The Process of Crystallization Syrup in Konya Sugar Factory

Abdullah Erdal Tumer, Bilgen Ayan Koc, Sabri Kocer
2017 International Journal of Intelligent Systems and Applications in Engineering  
In this study, a model has been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization  ...  Feed-forward backpropagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified.  ...  On the other hand, sugar production in the world is in the most important position in the food sector. Sugar factories use energy in large quantities.  ... 
doi:10.18201/ijisae.2017526691 fatcat:ti2blu3vf5gs3puov7ozq5ccqe
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