A novel feature extraction method using deep neural network for rolling bearing fault diagnosis

Rolling bearing fault diagnosis has received much attention because of its importance for the rotatory machinery. Feature extraction is the crucial part of rolling bearing fault diagnosis, which determines the diagnosis performance greatly. However, features extracted by many available methods canno...

Full description

Saved in:
Bibliographic Details
Main Authors: Weining Lu, Xueqian Wang, Chunchun Yang, Tao Zhang
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Data mining
Deep Neural Network
Fault diagnosis
Feature extraction
Rolling bearings
Training
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Rolling bearing fault diagnosis has received much attention because of its importance for the rotatory machinery. Feature extraction is the crucial part of rolling bearing fault diagnosis, which determines the diagnosis performance greatly. However, features extracted by many available methods cannot guarantee the sensitiveness to every interested fault category, which leads to incomplete diagnosis results and ability absence of handling with the situation that unknown-category fault appears. To solve this issue, the feature extraction method based on deep neural network (DNN) is proposed to extract a meaningful representation for bearing signal in this article. DNN is a new kind of machine learning tool with strong power of representation, which has been utilized as the feature extractors in lots of practical applications successfully. Afterwards, the effectiveness of this proposed approach is presented by using the actual rolling bearing data.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2015.7162328