Francisco Arellano-Espitia

About Francisco Arellano-Espitia

Francisco Arellano-Espitia, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Universidad Politécnica de Cataluña, specializes in the field of Electronics Engineering, Artificial Intelligence, Deep Learning, Condition Monitoring, Signal Processing.

His recent articles reflect a diverse array of research interests and contributions to the field:

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

Diagnosis electromechanical system by means CNN and SAE: An interpretable-learning study

Anomaly detection in electromechanical systems by means of deep-autoencoder

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems

Deep learning based condition monitoring approach applied to power quality

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

Deep-learning-based methodology for fault diagnosis in electromechanical systems

Francisco Arellano-Espitia Information

University

Position

MCIA Research Group

Citations(all)

78

Citations(since 2020)

78

Cited By

8

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

2

i10Index(since 2020)

2

Email

University Profile Page

Google Scholar

Francisco Arellano-Espitia Skills & Research Interests

Electronics Engineering

Artificial Intelligence

Deep Learning

Condition Monitoring

Signal Processing

Top articles of Francisco Arellano-Espitia

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

2023/9/12

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Diagnosis electromechanical system by means CNN and SAE: An interpretable-learning study

2022/5/24

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Anomaly detection in electromechanical systems by means of deep-autoencoder

2021/9/7

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Sensors

2021/8/30

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems

Sensors

2021/8/30

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Deep learning based condition monitoring approach applied to power quality

2020/9/8

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

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

2020/9/8

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Miguel Delgado Prieto
Miguel Delgado Prieto

H-Index: 13

Deep-learning-based methodology for fault diagnosis in electromechanical systems

Sensors

2020/7/16

Francisco Arellano-Espitia
Francisco Arellano-Espitia

H-Index: 2

Victor Martinez-Viol
Victor Martinez-Viol

H-Index: 2

Novel methods based on deep learning applied to condition monitoring in smart manufacturing processes

2020/3/25

See List of Professors in Francisco Arellano-Espitia University(Universidad Politécnica de Cataluña)

Co-Authors

academic-engine