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

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

Publikováno v: Sensors, Vol 21, Iss 17, p 5832 (2021)

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid

Externí odkaz: https://doaj.org/article/0a554884e5164385a7c53c06ecbcc0ea

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

Autor: Francisco Arellano-Espitia, Miguel Delgado-Prieto, Victor Martinez-Viol, Juan Jose Saucedo-Dorantes, Roque Alfredo Osornio-Rios

Publikováno v: Sensors, Vol 20, Iss 14, p 3949 (2020)

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on tradition

Externí odkaz: https://doaj.org/article/3b2008841560425982f448a2b0becc15

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

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

Publikováno v: Sensors (Basel, Switzerland)
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Sensors, Vol 21, Iss 5832, p 5832 (2021)
Sensors
Volume 21
Issue 17

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aeb4dd27f169289e667356647cbd3199
http://europepmc.org/articles/PMC8434472

Deep learning based condition monitoring approach applied to power quality

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

Publikováno v: ETFA
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)

Condition monitoring applied to power quality involves several techniques and procedures for the assessment of the electrical signal. Data-driven approaches are the most common methodologies supported on data and signal processing procedures. Electri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d49ff9e386768c549b3321c1868f0a75
https://hdl.handle.net/2117/330311

Novel condition monitoring approach based on hybrid feature extraction and neural network for assessing multiple faults in electromechanical systems

Autor: Rene de Jesus Romero-Troncoso, Roque Alfredo Osornio-Rios, Francisco Arellano-Espitia, Juan Jose Saucedo-Dorantes, Miguel Delgado-Prieto

Publikováno v: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Recercat. Dipósit de la Recerca de Catalunya
instname

New challenges involve the development of new condition monitoring approaches to avoid unexpected downtimes and to ensure the availability of machines during operating working conditions. The feature calculation from vibrations and stator currents is

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8ec9ac0e34609219eecd757daca5ea4
https://hdl.handle.net/2117/172842