Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production

Shpilevoy, V.; Shishov, A. A.; Skobelev, Petr; Kolbova, Elina; Kazanskaia, Daria; Shepilov, Ya.; Tsarev, Alexander

doi:10.3182/20130522-3-br-4036.00025

Cited by 18 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complexity of these environments with many simultaneous processes, most of which follow each other, as well as the enormous amount of data that sensors generate in production, can no longer be served by existing control systems based on the simple technology of recording or processing transactions. Therefore, multi-agent systems come to the light, where intelligent information agents form a network of decentralised and distributed intelligence [ 147 , 148 ]. Beside the existing solutions, these systems are not based on centralised control but are capable of collective self-configuration.…”

Section: Discussionmentioning

confidence: 99%

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Pech

Vrchota

Bednář

2021

Sensors

210

View full text Add to dashboard Cite

With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

show abstract

Section: Discussionmentioning

confidence: 99%

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Pech

Vrchota

Bednář

2021

Sensors

210

View full text Add to dashboard Cite

show abstract

“…Bhardwaj et al (2014) specified agent-based distributed railway system, that is focused on the issue of passing the trains in same direction by taking decisions through train agents negotiation and reduce the system delay for acceptable limit. In the study of Shpilevoy et al (2013), agent-based method of adaptive resource scheduling that presents functionality of multi-agent system-based smart factory. It also manages realtime resources in manufacturing workshops.…”

Section: Related Workmentioning

confidence: 99%

Handling temporal constraints in interaction protocols for intelligent multi-agent systems

Qasim

Iqbal

Aziz

et al. 2020

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

This research focuses on handling temporal constraints in interaction protocols for multi-agent systems. There is a dire need of standardized interaction protocols that can be used to handle timing aspects in real-time multi-agent system's negotiation. The most commonly used Foundation for Intelligent Physical Agents Protocol lacks the appropriate specification in this regard. In real-time systems timing constraint is a major concern for all of its tasks and goals. Agents require real-time responses and must eliminate the possibility of massive communication between them. The timing specification of these real-time multi-agent systems in which agents communicate with each other to achieve their goals within deadline will be of great value for their correct functioning. A high degree of dependability and predictability is expected from real-time software agents. The basis of our work is the standardized interaction protocols to support the communication between agents in real-time environment and this is possible via message passing. By incorporation of welldefined timing parameters in Foundation for Intelligent Physical Agents performatives, we have enabled them to be used in any realtime multi-agent's communication. We demonstrate the usage and effectiveness of our proposed real-time performatives using a case study of monitoring boats in marine reserves in which the agents interact with each other to accomplish their goals.

show abstract

“…Metzger & Polaków (2011) present the use of MAS in a continuous process automation solution. Shpilevoy et al (2013) apply MAS to the modelling of aircraft engine manufacturing production planning, defining agents as production orders, organization, machines, assemblers, products, and operation. Adeyeri et al (2015) present a literary review of articles from 2003 to 2014 on the use of agents in manufacturing, specifically in reconfigurable manufacturing systems.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Highlighting the benefits of Industry 4.0 for production: an agent-based simulation approach

Cavata¹,

Massote²,

Maia³

et al. 2020

Gest. Prod.

View full text Add to dashboard Cite

Abstract: Advanced Manufacturing or Industry 4.0 concepts bring new advances and challenges to current industrial processes. Such concepts are not always well understood and their results in terms of production performance may not be clear. This work proposes a comparison between a traditional manufacturing process and an advanced manufacturing process, both modelled by a multiagent society. In the traditional manufacturing simulation, the agents follow the defined times of each process, including the maintenance times. In the advanced manufacturing simulation, the decision about when to stop a piece of equipment for maintenance is defined by the agent according to data received from sensors and the definitions of the process. The results indicate a significant improvement in equipment usage and consequently higher production in the same time interval. The process simulation clearly indicates that the application of advanced manufacturing concepts in industry is relevant in order to increase the efficiency of production processes. Among the main concepts introduced in advanced manufacturing models are the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Artificial Intelligence (AI). The models generated are computationally simulated using an agent-based simulation method from the software AnyLogic. The results obtained should contribute to encouraging small and medium sized enterprises to adopt the concepts of Industry 4.0 in their businesses.

show abstract

Multi-agent system “Smart Factory” for real-time workshop management in aircraft jet engines production

Cited by 18 publications

References 2 publications

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Handling temporal constraints in interaction protocols for intelligent multi-agent systems

Highlighting the benefits of Industry 4.0 for production: an agent-based simulation approach

Contact Info

Product

Resources

About