Improved DCNN Based on Multi-Source Signals for Motor Compound Fault Diagnosis

Improved DCNN Based on Multi-Source Signals for Motor Compound Fault Diagnosis

Induction motors, the key equipment for rotating machinery, are prone to compound faults, such as a broken rotor bars and bearing defects. It is difficult to extract fault features and identify faults from a single signal because multiple fault features overlap and interfere with each other in a com...

Full description

Bibliographic Details
Main Authors: Xiaoyun Gong, Zeheng Zhi, Kunpeng Feng, Wenliao Du, Tao Wang
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Machines
Subjects:
fault diagnosis
compound faults
information fusion
learning rate decay
convolutional neural network
Online Access:https://www.mdpi.com/2075-1702/10/4/277

Internet

https://www.mdpi.com/2075-1702/10/4/277

Similar Items