Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems
Abstract: In this letter, supervised machine learning classifiers are compared with clustering algorithms in the categorization of malfunctioned rotor systems. For this purpose, a dataset containing 660 faulted rotor‐bearing systems—that are, unbalanced, misaligned, and cracked—is used. Samples are created utilizing the finite element method in MATLAB. A sequential forward selection (SFS) method is employed to reduce the number of features in the signal‐processing stage after the feature extraction phase in the time, frequency, and time–frequency domains. The outcomes of three supervised algorithms—support vector machine, k‐nearest neighbors, and ensemble learning, as well as one unsupervised procedure, e.g., k‐Means clustering, are compared. In the latter method, to find the optimal number of clusters, the Calinski–Harabasz criterion is applied. The findings represent that, even though the supervised methods' acquired accuracies are noticeably higher (97.7% in the validation stage), using clustering algorithms can be beneficial in a variety of real‐time condition monitoring applications in rotating machines where the type, extent, and location of the damage are unknown.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems ; volume:411 ; number:1 ; year:2023 ; extent:4
Macromolecular symposia ; 411, Heft 1 (2023) (gesamt 4)
- Creator
-
Rezazadeh, Nima
Felaco, Amelia
Fallahy, Shila
Lamanna, Giuseppe
- DOI
-
10.1002/masy.202200219
- URN
-
urn:nbn:de:101:1-2023101615365697834459
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:52 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Rezazadeh, Nima
- Felaco, Amelia
- Fallahy, Shila
- Lamanna, Giuseppe
Other Objects (12)
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Aerostatic bearing for large rotor
Applications of supervised and unsupervised ensemble methods
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Detecting insurance fraud using supervised and unsupervised machine learning
Supervised and unsupervised analyses in next generation sequencing data
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Supervised and unsupervised learning in the prediction of academic achievement
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Aerostatic bearing for large rotor
Applications of supervised and unsupervised ensemble methods
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Detecting insurance fraud using supervised and unsupervised machine learning
Supervised and unsupervised analyses in next generation sequencing data
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning
Supervised and unsupervised learning in the prediction of academic achievement
Statistical models for unsupervised, semi-supervised and supervised transliteration mining
Aerostatic bearing for large rotor
Applications of supervised and unsupervised ensemble methods
Supervised and unsupervised ensemble methods and their applications
Active data selection in supervised and unsupervised learning
Active data selection in supervised and unsupervised learning
Detecting insurance fraud using supervised and unsupervised machine learning
Supervised and unsupervised analyses in next generation sequencing data
Riemannian Flows for Supervised and Unsupervised Geometric Image Labeling
Supervised and unsupervised ensemble learning for the semantic web
Kernel based algorithms for mining huge data sets : supervised, semi-supervised, and unsupervised learning