Early Fault Detection via Multiple Feature Fusion and Ensemble Learning

Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a generic and robust model for different equipment. Most existing EFD methods focus on learning fault representation...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE sensors journal Jg. 24; H. 5; S. 7196 - 7204
Hauptverfasser:	Song, Wenbin, Wu, Di, Shen, Weiming, Boulet, Benoit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Datasets Early fault detection (EFD) Ensemble learning Fault detection Feature extraction Frequency domain analysis Maintenance costs Mechanical systems Noise reduction one-class support vector machine (SVM) Representations Rotating machinery Rotating machines stacked denoising autoencoder (SDAE) Stacking Support vector machines Systems stability
ISSN:	1530-437X, 1558-1748
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!