@article{drakaki_recent_2021,
	series = {Proceedings of the 2nd {International} {Conference} on {Industry} 4.0 and {Smart} {Manufacturing} ({ISM} 2020)},
	title = {Recent {Developments} {Towards} {Industry} 4.0 {Oriented} {Predictive} {Maintenance} in {Induction} {Motors}},
	volume = {180},
	issn = {1877-0509},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050921003999},
	doi = {10.1016/j.procs.2021.01.345},
	abstract = {Predictive maintenance (PdM) for smart manufacturing and Industry 4.0 has been associated with manufacturing intelligence supported by Artificial Intelligence (AI). Therefore, PdM also relies on the smart manufacturing technologies including cyber-physical system (CPS) and big data analytics. The multi-agent system (MAS) technology and deep learning (DL) have shown the capacity to provide efficient tools for the implementation of PdM in a CPS enabled smart industrial production system gaining feedback from big data analytics. Induction motors (IM) constitute the main power source in the industrial production environment and therefore their maintenance and early fault detection and diagnosis (FD/D) is a critical process. Neural network (NN) based FD/D of IM has been widely used in order to identify different fault types. DL methods have recently emerged for FD/D of IM and can efficiently analyze massive data coming from different machine sensors. The MAS has recently been used in combination with artificial NNs as a decision support tool for FD/D of IM. This paper aims to provide a review of recent trends in PdM of IM focusing on MAS and DL based FD/D methods that have emerged in the last 5 years due to their potential to be implemented in a smart manufacturing system. A discussion of the presented methods is given in order to present the recent developments and trends and provide future directions for research.},
	language = {en},
	urldate = {2021-02-22},
	journal = {Procedia Computer Science},
	author = {Drakaki, Maria and Karnavas, Yannis L. and Tzionas, Panagiotis and Chasiotis, Ioannis D.},
	month = jan,
	year = {2021},
	keywords = {Predictive maintenance, deep learning, fault detection, fault diagnosis, induction motor, machinery health management, multi-agent system, neural networks},
	pages = {943--949},
}

{"_id":"WPbnzDHuejKoTh8vh","bibbaseid":"drakaki-karnavas-tzionas-chasiotis-recentdevelopmentstowardsindustry40orientedpredictivemaintenanceininductionmotors-2021","author_short":["Drakaki, M.","Karnavas, Y. L.","Tzionas, P.","Chasiotis, I. D."],"bibdata":{"bibtype":"article","type":"article","series":"Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)","title":"Recent Developments Towards Industry 4.0 Oriented Predictive Maintenance in Induction Motors","volume":"180","issn":"1877-0509","url":"https://www.sciencedirect.com/science/article/pii/S1877050921003999","doi":"10.1016/j.procs.2021.01.345","abstract":"Predictive maintenance (PdM) for smart manufacturing and Industry 4.0 has been associated with manufacturing intelligence supported by Artificial Intelligence (AI). Therefore, PdM also relies on the smart manufacturing technologies including cyber-physical system (CPS) and big data analytics. The multi-agent system (MAS) technology and deep learning (DL) have shown the capacity to provide efficient tools for the implementation of PdM in a CPS enabled smart industrial production system gaining feedback from big data analytics. Induction motors (IM) constitute the main power source in the industrial production environment and therefore their maintenance and early fault detection and diagnosis (FD/D) is a critical process. Neural network (NN) based FD/D of IM has been widely used in order to identify different fault types. DL methods have recently emerged for FD/D of IM and can efficiently analyze massive data coming from different machine sensors. The MAS has recently been used in combination with artificial NNs as a decision support tool for FD/D of IM. This paper aims to provide a review of recent trends in PdM of IM focusing on MAS and DL based FD/D methods that have emerged in the last 5 years due to their potential to be implemented in a smart manufacturing system. A discussion of the presented methods is given in order to present the recent developments and trends and provide future directions for research.","language":"en","urldate":"2021-02-22","journal":"Procedia Computer Science","author":[{"propositions":[],"lastnames":["Drakaki"],"firstnames":["Maria"],"suffixes":[]},{"propositions":[],"lastnames":["Karnavas"],"firstnames":["Yannis","L."],"suffixes":[]},{"propositions":[],"lastnames":["Tzionas"],"firstnames":["Panagiotis"],"suffixes":[]},{"propositions":[],"lastnames":["Chasiotis"],"firstnames":["Ioannis","D."],"suffixes":[]}],"month":"January","year":"2021","keywords":"Predictive maintenance, deep learning, fault detection, fault diagnosis, induction motor, machinery health management, multi-agent system, neural networks","pages":"943–949","bibtex":"@article{drakaki_recent_2021,\n\tseries = {Proceedings of the 2nd {International} {Conference} on {Industry} 4.0 and {Smart} {Manufacturing} ({ISM} 2020)},\n\ttitle = {Recent {Developments} {Towards} {Industry} 4.0 {Oriented} {Predictive} {Maintenance} in {Induction} {Motors}},\n\tvolume = {180},\n\tissn = {1877-0509},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1877050921003999},\n\tdoi = {10.1016/j.procs.2021.01.345},\n\tabstract = {Predictive maintenance (PdM) for smart manufacturing and Industry 4.0 has been associated with manufacturing intelligence supported by Artificial Intelligence (AI). Therefore, PdM also relies on the smart manufacturing technologies including cyber-physical system (CPS) and big data analytics. The multi-agent system (MAS) technology and deep learning (DL) have shown the capacity to provide efficient tools for the implementation of PdM in a CPS enabled smart industrial production system gaining feedback from big data analytics. Induction motors (IM) constitute the main power source in the industrial production environment and therefore their maintenance and early fault detection and diagnosis (FD/D) is a critical process. Neural network (NN) based FD/D of IM has been widely used in order to identify different fault types. DL methods have recently emerged for FD/D of IM and can efficiently analyze massive data coming from different machine sensors. The MAS has recently been used in combination with artificial NNs as a decision support tool for FD/D of IM. This paper aims to provide a review of recent trends in PdM of IM focusing on MAS and DL based FD/D methods that have emerged in the last 5 years due to their potential to be implemented in a smart manufacturing system. A discussion of the presented methods is given in order to present the recent developments and trends and provide future directions for research.},\n\tlanguage = {en},\n\turldate = {2021-02-22},\n\tjournal = {Procedia Computer Science},\n\tauthor = {Drakaki, Maria and Karnavas, Yannis L. and Tzionas, Panagiotis and Chasiotis, Ioannis D.},\n\tmonth = jan,\n\tyear = {2021},\n\tkeywords = {Predictive maintenance, deep learning, fault detection, fault diagnosis, induction motor, machinery health management, multi-agent system, neural networks},\n\tpages = {943--949},\n}\n\n\n\n","author_short":["Drakaki, M.","Karnavas, Y. L.","Tzionas, P.","Chasiotis, I. 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