Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input
Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D tempor...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2017-01-01
|
Series: | MATEC Web of Conferences |
Online Access: | https://doi.org/10.1051/matecconf/20179513001 |
id |
doaj-28290c1df5af49659723136d7057ece4 |
---|---|
record_format |
Article |
spelling |
doaj-28290c1df5af49659723136d7057ece42021-02-02T08:15:09ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-01951300110.1051/matecconf/20179513001matecconf_icmme2017_13001Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as InputZhang WeiPeng GaoliangLi ChuanhaoPeriodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN) is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.https://doi.org/10.1051/matecconf/20179513001 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhang Wei Peng Gaoliang Li Chuanhao |
spellingShingle |
Zhang Wei Peng Gaoliang Li Chuanhao Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input MATEC Web of Conferences |
author_facet |
Zhang Wei Peng Gaoliang Li Chuanhao |
author_sort |
Zhang Wei |
title |
Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input |
title_short |
Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input |
title_full |
Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input |
title_fullStr |
Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input |
title_full_unstemmed |
Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input |
title_sort |
bearings fault diagnosis based on convolutional neural networks with 2-d representation of vibration signals as input |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2017-01-01 |
description |
Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN) is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems. |
url |
https://doi.org/10.1051/matecconf/20179513001 |
work_keys_str_mv |
AT zhangwei bearingsfaultdiagnosisbasedonconvolutionalneuralnetworkswith2drepresentationofvibrationsignalsasinput AT penggaoliang bearingsfaultdiagnosisbasedonconvolutionalneuralnetworkswith2drepresentationofvibrationsignalsasinput AT lichuanhao bearingsfaultdiagnosisbasedonconvolutionalneuralnetworkswith2drepresentationofvibrationsignalsasinput |
_version_ |
1724297557771812864 |