LSTM network in bilateral teleoperation of a skid-steering robot

Slawiñski, Emanuel; Rossomando, Francisco; Chicaiza, Fernando A.; Moreno-Valenzuela, Javier; Mut, Vicente

doi:10.1016/j.neucom.2024.128248

Neurocomputing

2024

DOI: 10.1016/j.neucom.2024.128248

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LSTM network in bilateral teleoperation of a skid-steering robot

Emanuel Slawiñski,

Francisco Rossomando,

Fernando A. Chicaiza

et al.

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2024

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Early Fault Detection in Paper Machine Motors Using Machine Learning

Chuchico,

Acosta Agudelo

2024

Int. Jour. Eng. Ins.

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This research addresses the application of a neural network as a tool for early fault detection in the motors of a paper machine under a simulated environment. It proposes the analysis of variables from a torque control loop. The data for training and validating the model is obtained through the simulation of Direct Torque Control (DTC) of an AC motor in Simscape within Simulink. Both normal and faulty operating modes are considered. Under these two scenarios, various speed setpoints are configured, and the necessary data for training the developed model is collected

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Early Fault Detection in Paper Machine Motors Using Machine Learning

Chuchico,

Acosta Agudelo

2024

Int. Jour. Eng. Ins.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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LSTM network in bilateral teleoperation of a skid-steering robot

Cited by 1 publication

References 20 publications

Early Fault Detection in Paper Machine Motors Using Machine Learning

Early Fault Detection in Paper Machine Motors Using Machine Learning

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