A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

Aiming at the fault diagnosis issue of rotating machinery, a novel method based on the deep learning theory is presented in this paper. By combining one-dimensional convolutional neural networks (1D-CNN) with self-normalizing neural networks (SNN), the proposed method can achieve high fault identifi...

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Bibliographic Details
Main Authors: Jingli Yang, Shuangyan Yin, Yongqi Chang, Tianyu Gao
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
one-dimensional convolutional neural networks
self-normalizing neural networks
SeLU
α-dropout
fault diagnosis
Online Access:https://www.mdpi.com/1424-8220/20/14/3837

Internet

https://www.mdpi.com/1424-8220/20/14/3837

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