Luis-Renato Ortega
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Luis-Renato Ortega has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Diego Cabrera
South China University of Technology
2 shared publications - Mariela Cerrada
Universidad Estatal de Milagro
2 shared publications - René-Vinicio Sánchez
Universidad Politécnica Salesiana
2 shared publications - Deiver Jiménez-Santín
GIDTEC Univ Politecn Salesiana, Cuenca 010105, Azuay, Ecuador
1 shared publication - Fausto Pedro García Márquez
Universidad Castilla-La Mancha
1 shared publication - Jean Carlo Macancela
GIDTEC, Universidad Politécnica Salesiana, Cuenca 010105, Ecuador
1 shared publication
Latest Publications
- Open Access
Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey
This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.
Open AccessEvaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey
This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.