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SMILE: Smart Monitoring IoT Learning Ecosystem

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@article{IJASEIT11144,
   author = {Roberta Avanzato and Francesco Beritelli and Francesco Di Franco and Michele Russo},
   title = {SMILE: Smart Monitoring IoT Learning Ecosystem},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {1},
   year = {2020},
   pages = {413--419},
   keywords = {unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.},
   abstract = {

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11144},    doi = {10.18517/ijaseit.10.1.11144} }

EndNote

%A Avanzato, Roberta
%A Beritelli, Francesco
%A Franco, Francesco Di
%A Russo, Michele
%D 2020
%T SMILE: Smart Monitoring IoT Learning Ecosystem
%B 2020
%9 unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.
%! SMILE: Smart Monitoring IoT Learning Ecosystem
%K unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.
%X 

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11144 %R doi:10.18517/ijaseit.10.1.11144 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 1 %@ 2088-5334

IEEE

Roberta Avanzato,Francesco Beritelli,Francesco Di Franco and Michele Russo,"SMILE: Smart Monitoring IoT Learning Ecosystem," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 1, pp. 413-419, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.1.11144.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Avanzato, Roberta
AU  - Beritelli, Francesco
AU  - Franco, Francesco Di
AU  - Russo, Michele
PY  - 2020
TI  - SMILE: Smart Monitoring IoT Learning Ecosystem
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 1
Y2  - 2020
SP  - 413
EP  - 419
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.
N2  - 

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11144 DO - 10.18517/ijaseit.10.1.11144

RefWorks

RT Journal Article
ID 11144
A1 Avanzato, Roberta
A1 Beritelli, Francesco
A1 Franco, Francesco Di
A1 Russo, Michele
T1 SMILE: Smart Monitoring IoT Learning Ecosystem
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 1
YR 2020
SP 413
OP 419
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.
AB 

In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11144 DO - 10.18517/ijaseit.10.1.11144