Artificial intelligence for predictive maintenance in the railcar learning factories

Daniyan, Ilesanmi; Mpofu, Khumbulani; Oyesola, Moses; Ramatsetse, Boitumelo; Adeodu, Adefemi

doi:10.1016/j.promfg.2020.04.032

Cited by 41 publications

(11 citation statements)

References 18 publications

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“…Journal of Engineering concepts are learned in theory work in a hands-on and industry-related environment [200]. Elaborately, LFs are a 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 (i.e., the gap between academia and industry) [206,207]. e promising strength of LFs is the ability to solve problems in a structured way which is an essential competence of people in a factory, from the shop floor operator to the management level factory [208].…”

Section: Learning Factorymentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

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Section: Learning Factorymentioning

confidence: 99%

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Bongomin

Yemane

Kembabazi

et al. 2020

Journal of Engineering

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show abstract

“…For this purpose, the respective learning factories must be precisely tailored to the topics taught to enable the participants to gain the necessary competencies. Existing learning factories address ML focusing on tasks such as predictive maintenance, quality control or activity and position recognition (Daniyan et al 2020;Hofmann et al 2020;Oberc et al 2020). To teach participants the necessary competencies to setup a data mining process according to CRISP-DM using ML, a multi-phase learning factory training is required.…”

Section: Discussionmentioning

confidence: 99%

“…In a recently published study Martinez et al show the rapid increase in publications dealing with learning factories since 2015 (Martinez et al 2020). Lately ML methods have already been implemented in learning factories addressing predictive maintenance (Daniyan et al 2020), quality control (Oberc et al 2020), activity and position recognition (Hofmann et al 2020;Zhang et al 2020) or process mining on end-to-end order processing (Schuh et al 2020). For the training presented here products and the environment of the learning factory already existing at the Leuphana University of Lüneburg are used, allowing access to the ecosystem data and IT systems.…”

Section: Learning Factoriesmentioning

confidence: 99%

How a learning factory approach can help to increase the un- derstanding of the application of machine learning on produc- tion planning and control tasks.

Rokoss¹,

Kramer²,

Schmidt³

2021

Competence Development and Learning Assistance Systems for the Data-Driven Future

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Technological progress and increasing digitalization offer many opportunities to production companies, but also continually present them with new challenges. The automation of processes is progressing in manufacturing areas and technical support systems, such as human-robot collaboration, are leading to significant changes in workflows. However, in other areas of companies large parts of the work are still done by humans. This is partly the case with the use of production data. Although much data is already collected and sorted automatically, the final evaluation of this data and especially decision-making is often done by humans. In particular, this is the case for decisions that cannot clearly be made based on conditional programming. The use of machine learning (ML) represents a promising approach to make such complex decisions automatically. A sharp increase in scientific publications in the recent years demonstrates the trend that more and more companies and institutions are looking into the use of machine learning in production. Since ML is beeing applied across several industries, the resulting massive shortage of skilled workers in the field of ML has to be addressed in short and medium terms by training and educating existing employees in production companies. A contemporary approach to building competencies in dealing with problems in the manufacturing sector is the use of learning factories as a knowledge transfer enabler. They offer learners the opportunity to try out methods in a realistic environment without having to fear negative consequences for the company. The results of actions performed by participants can be experienced directly without any time delay, resulting in better learning results compared to conventional face-to-face teaching. This chapter shows how learning factories can support teaching machine learning methods in the field of PPC. For this purpose, the determination of lead times using real data sets is addressed with ML-based methods. Parallelly, the competencies required for the respective tasks were extracted. Based on this, elements of a learning factory were designed that simplifies the considered processes, so that the problem can be easily understood by learners. The last part of the chapter describes several learning factory game phases aiming on teaching the identified competencies. The described learning factory enables participants to setup ML-based projects in the context of manufacturing.

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“…Elaborately, LFs 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 (i.e. the gap between academia and industry) [206,207]. The promising strength of LFs is the ability to solve problems in a structured way is an essential competence of people in a factory, from the shop floor operator to the management level factory [208].…”

Section: Learning Factorymentioning

confidence: 99%

The Hype and Disruptive Technologies of Industry 4.0 in Major Industrial Sectors: A State of the Art

Bongomin¹,

Yemane²,

Kembabazi³

et al. 2020

Preprint

View full text Add to dashboard Cite

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

show abstract

Artificial intelligence for predictive maintenance in the railcar learning factories

Cited by 41 publications

References 18 publications

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

How a learning factory approach can help to increase the un- derstanding of the application of machine learning on produc- tion planning and control tasks.

The Hype and Disruptive Technologies of Industry 4.0 in Major Industrial Sectors: A State of the Art

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