Marcelo Ramos Martins

Prof. Dr. Marcelo Ramos Martins

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Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2013 in Reliability Engineering & System Safety
Verified
Journal Article
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MMMarcelo Ramos Martins
Marcelo Ramos Martins
MMMarcos Coelho Maturana
Marcos Coelho Maturana

12 April 2010 in Risk Analysis
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Journal Article
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MMMarcelo Ramos Martins
Marcelo Ramos Martins
MMMarcos Coelho Maturana
Marcos Coelho Maturana

1 September 2021 in Sensors
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Journal Article
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JBJoaquín Figueroa Barraza
Joaquín Figueroa Barraza
Enrique DroguettEnrique Droguett
Enrique Droguett
MMMarcelo Ramos Martins
Marcelo Ramos Martins
In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.


1 September 2016 in Journal of Loss Prevention in the Process Industries
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Journal Article
Closed access
ASAdriana Miralles Schleder
Adriana Miralles Schleder
MMMarcelo Ramos Martins
Marcelo Ramos Martins

18 August 2017 in The Canadian Journal of Chemical Engineering
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MRMarilia Abílio Ramos
Marilia Abílio Ramos
Enrique DroguettEnrique Droguett
Enrique Droguett
AMAli Mosleh
Ali Mosleh
MMMarcio Moura
Marcio Moura
MMMarcelo Ramos Martins
Marcelo Ramos Martins