@article{Kotsiopoulos2021,

title = {Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm},

author = { T. Kotsiopoulos and P. Sarigiannidis and D. Ioannidis and D. Tzovaras},

url = {https://www.researchgate.net/publication/346545781_Machine_Learning_and_Deep_Learning_in_Smart_Manufacturing_The_Smart_Grid_Paradigm},

doi = {10.1016/j.cosrev.2020.100341},

year  = {2021},

date = {2021-05-01},

journal = {Computer Science Review},

volume = {40},

pages = {100341},

publisher = {Elsevier BV},

abstract = {Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.},

keywords = {Deep Learning, Industrial AI, Industry 4.0, machine learning, Smart Grid},

pubstate = {published},

tppubtype = {article}

}