Imbalanced Fault Diagnosis of Rotating Machinery Based on Deep Generative Adversarial Networks with Gradient Penalty

In practical industrial application, the fault samples collected from rotating machinery are frequently unbalanced, which will create difficulties when it comes to diagnosis. Besides, the variation of working conditions and noise factors will further reduce the diagnosis’s accuracy and stability. Co...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Processes 2021-10, Vol.9 (10), p.1751
Hauptverfasser:	Luo, Junqi, Zhu, Liucun, Li, Quanfang, Liu, Daopeng, Chen, Mingyou
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Back propagation networks Belief networks Fault diagnosis Generative adversarial networks Industrial applications Machine learning Machinery Neural networks Noise Noise reduction Rotating machinery Signal processing Support vector machines Trends Wavelet transforms
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!