Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

The traditional intelligent diagnosis methods of rotating machinery generally require feature extraction of the raw signals in advance. However, it is a very time-consuming and laborious process for extracting the sensitive feature information to improve classification performance. Deep learning met...

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Bibliographic Details
Main Authors: Yibing Li, Li Zou, Li Jiang, Xiangyu Zhou
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Deep belief network (DBN)
one-dimensional convolutional neural network (1D-CNN)
rotating machinery
feature extraction
intelligent fault diagnosis
Online Access:https://ieeexplore.ieee.org/document/8901430/

Internet

https://ieeexplore.ieee.org/document/8901430/

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