Francisco Arellano-Espitia

According to our database, Francisco Arellano-Espitia authored at least 11 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of five.

2024

Deep learning based methodologies applied to industrial electromechanical systems monitoring

Francisco Arellano-Espitia

PhD thesis, 2024

2023

Deep Learning-Based Partial Transfer Fault Diagnosis Methodology for Electromechanical Systems.

Francisco Arellano-Espitia, Miguel Delgado Prieto, Joan Valls-Perez, Juan Jose Saucedo Dorantes, Roque Alfredo Osornio-Rios

Proceedings of the 28th IEEE International Conference on Emerging Technologies and Factory Automation, 2023

2022

Diagnosis Electromechanical System by Means CNN and SAE: An Interpretable-Learning Study.

Francisco Arellano-Espitia, Miguel Delgado Prieto, Víctor Martínez-Viol, Juan Jose Saucedo Dorantes, Roque Alfredo Osornio-Rios

Proceedings of the 5th IEEE International Conference on Industrial Cyber-Physical Systems, 2022

2021

Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings.

Juan Jose Saucedo Dorantes, Francisco Arellano-Espitia, Miguel Delgado Prieto, Roque Alfredo Osornio-Rios

Sensors, 2021

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems.

Francisco Arellano-Espitia, Miguel Delgado Prieto, Artvin Darien Gonzalez-Abreu, Juan Jose Saucedo Dorantes, Roque Alfredo Osornio-Rios

Sensors, 2021

Anomaly Detection in Electromechanical Systems by means of Deep-Autoencoder.

Francisco Arellano-Espitia, Miguel Delgado Prieto, Víctor Martínez-Viol, Ángel Fernandez Sobrino, Roque Alfredo Osornio-Rios

Proceedings of the 26th IEEE International Conference on Emerging Technologies and Factory Automation, 2021

2020

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems.

Francisco Arellano-Espitia, Miguel Delgado Prieto, Víctor Martínez-Viol, Juan Jose Saucedo Dorantes, Roque Alfredo Osornio-Rios

Sensors, 2020

Deep Learning based Condition Monitoring approach applied to Power Quality.

Artvin Darien Gonzalez-Abreu, Juan José Saucedo-Dorantes, Roque Alfredo Osornio-Rios, Francisco Arellano-Espitia, Miguel Delgado Prieto

Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation, 2020

Analysis of Machine Learning based Condition Monitoring Schemes Applied to Complex Electromechanical Systems.

Francisco Arellano-Espitia, Artvin Darien Gonzalez-Abreu, Miguel Delgado Prieto, Juan José Saucedo-Dorantes, Roque Alfredo Osornio-Rios

Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation, 2020

2019

Autoencoder based feature reduction analysis applied to electromechanical systems condition monitoring.

Francisco Arellano-Espitia, Juan Jose Saucedo Dorantes, Miguel Delgado Prieto, Roque Alfredo Osornio-Rios

Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation, 2019

2018

Statistical data fusion as diagnosis scheme applied to a kinematic chain.

Francisco Arellano-Espitia, Juan Jose Saucedo Dorantes, Roque Alfredo Osornio-Rios, Miguel Delgado Prieto, Jesus Adolfo Carino-Corrales, René de Jesús Romero-Troncoso

Proceedings of the IEEE International Conference on Industrial Technology, 2018