Feature models and condition visualization for rotating machinery fault diagnosis. - Thomas W. Rauber; Francisco de Assis Boldt; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (ICECS, 2013)

Computational intelligence for automatic diagnosis of submersible motor pump conditions in offshore oil exploration. - Thomas W. Rauber; Francisco de Assis Boldt; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (ICECS, 2013)

Evaluation of the Extreme Learning Machine for automatic fault diagnosis of the Tennessee Eastman chemical process. - Francisco de Assis Boldt; Thomas W. Rauber; Flávio Miguel Varejão (IECON, 2014)

Performance analysis of extreme learning machine for automatic diagnosis of electrical submersible pump conditions. - Francisco de Assis Boldt; Thomas W. Rauber; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (INDIN, 2014)

Single Sequence Fast Feature Selection for High-Dimensional Data. - Francisco de Assis Boldt; Thomas W. Rauber; Flávio Miguel Varejão (ICTAI, 2015)

Fast feature selection using hybrid ranking and wrapper approach for automatic fault diagnosis of motorpumps based on vibration signals. - Francisco de Assis Boldt; Thomas W. Rauber; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (INDIN, 2015)

Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis. - Thomas W. Rauber; Francisco de Assis Boldt; Flávio Miguel Varejão (IEEE Trans. Ind. Electron., 2015)

Kernel and random extreme learning machine applied to submersible motor pump fault diagnosis. - Thomas Walter Rauber; Thiago Oliveira-Santos; Francisco de Assis Boldt; Alexandre Rodrigues 001; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (IJCNN, 2017)

Binary feature selection classifier ensemble for fault diagnosis of submersible motor pump. - Francisco de Assis Boldt; Thomas Walter Rauber; Thiago Oliveira-Santos; Alexandre Rodrigues 001; Flávio Miguel Varejão; Marcos Pellegrini Ribeiro (ISIE, 2017)

Cascade Feature Selection and ELM for automatic fault diagnosis of the Tennessee Eastman process. - Francisco de Assis Boldt; Thomas W. Rauber; Flávio Miguel Varejão (Neurocomputing, 2017)

Feature selection for multivariate contribution analysis in fault detection and isolation. - Thomas W. Rauber; Francisco de Assis Boldt; Celso José Munaro (J. Frankl. Inst., 2020)

A Proposal to Mitigate Similarity Bias for the Paderborn Bearing Data Set. - Lúcio Antônio Stange Venturim; Francisco de Assis Boldt (IECON, 2021)

An experimental methodology to evaluate machine learning methods for fault diagnosis based on vibration signals. - Thomas Walter Rauber; Antonio Luiz da Silva Loca; Francisco de Assis Boldt; Alexandre Loureiros Rodrigues; Flávio Miguel Varejão (Expert Syst. Appl., 2021)

Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation. - Carlos Eduardo G. R. Alves; Francisco de Assis Boldt; Thiago M. Paixão (SIBGRAPI, 2024)

Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring. - Mert Sehri; Igor Varejão; Zehui Hua; Vitor Berger Bonella; Adriano Santos; Francisco de Assis Boldt; Patrick Dumond; Flávio Miguel Varejão (CoRR, 2025)

Selective Embedding for Deep Learning. - Mert Sehri; Zehui Hua; Francisco de Assis Boldt; Patrick Dumond (CoRR, 2025)