Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the...

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Bibliographic Details
Main Authors: Xingchen Liu, Qicai Zhou, Jiong Zhao, Hehong Shen, Xiaolei Xiong
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Sensors
Subjects:
intelligent diagnosis
anti-noise diagnosis
convolutional neural network
convolutional autoencoder
rotating machinery
Online Access:https://www.mdpi.com/1424-8220/19/4/972

Internet

https://www.mdpi.com/1424-8220/19/4/972

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