Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solv...

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Main Authors: Hongmei Liu, Lianfeng Li, Jian Ma
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2016/6127479
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author Hongmei Liu
Lianfeng Li
Jian Ma
author_facet Hongmei Liu
Lianfeng Li
Jian Ma
author_sort Hongmei Liu
collection DOAJ
description The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.
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id doaj-art-dc35be2e73024045be88fd96170d6f08
institution Kabale University
issn 1070-9622
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language English
publishDate 2016-01-01
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record_format Article
series Shock and Vibration
spelling doaj-art-dc35be2e73024045be88fd96170d6f082025-02-03T01:02:08ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/61274796127479Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound SignalsHongmei Liu0Lianfeng Li1Jian Ma2School of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing, ChinaThe main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.http://dx.doi.org/10.1155/2016/6127479
spellingShingle Hongmei Liu
Lianfeng Li
Jian Ma
Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
Shock and Vibration
title Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
title_full Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
title_fullStr Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
title_full_unstemmed Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
title_short Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
title_sort rolling bearing fault diagnosis based on stft deep learning and sound signals
url http://dx.doi.org/10.1155/2016/6127479
work_keys_str_mv AT hongmeiliu rollingbearingfaultdiagnosisbasedonstftdeeplearningandsoundsignals
AT lianfengli rollingbearingfaultdiagnosisbasedonstftdeeplearningandsoundsignals
AT jianma rollingbearingfaultdiagnosisbasedonstftdeeplearningandsoundsignals