Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. H...

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Autor principal:	Shih-Lin Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	VMD-DenseNet intelligent fault diagnosis bearing fault Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/fffe8e3fc1e147b3b564b08d52d23857
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spelling	oai:doaj.org-article:fffe8e3fc1e147b3b564b08d52d238572021-11-25T18:56:40ZIntelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet10.3390/s212274671424-8220https://doaj.org/article/fffe8e3fc1e147b3b564b08d52d238572021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7467https://doaj.org/toc/1424-8220Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.Shih-Lin LinMDPI AGarticleVMD-DenseNetintelligent fault diagnosisbearing faultChemical technologyTP1-1185ENSensors, Vol 21, Iss 7467, p 7467 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	VMD-DenseNet intelligent fault diagnosis bearing fault Chemical technology TP1-1185
spellingShingle	VMD-DenseNet intelligent fault diagnosis bearing fault Chemical technology TP1-1185 Shih-Lin Lin Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
description	Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.
format	article
author	Shih-Lin Lin
author_facet	Shih-Lin Lin
author_sort	Shih-Lin Lin
title	Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
title_short	Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
title_full	Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
title_fullStr	Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
title_full_unstemmed	Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
title_sort	intelligent fault diagnosis and forecast of time-varying bearing based on deep learning vmd-densenet
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fffe8e3fc1e147b3b564b08d52d23857
work_keys_str_mv	AT shihlinlin intelligentfaultdiagnosisandforecastoftimevaryingbearingbasedondeeplearningvmddensenet
_version_	1718410566312132608

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

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