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THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

WU DingHai / REN GuoQuan / WANG HuaiGuang / ZHANG YunQiang

Deep learning is good at abstract features from massive data and has good generalization ability,which has attracted more and more researchers’ attention. The Convolutional Neural Network（ CNN） is a classic structure of deep learing and which is being widely and successfully used in the fields of computer vision,target detection,natural language processing,and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis,this paper introduces the structure of CNN,and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type,network structure design and migration learning. The problems of deep feature extraction and visualization are also discussed,and finally,the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN are prospected.

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THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

WU DingHai / REN GuoQuan / WANG HuaiGuang / ZHANG YunQiang

Deep learning is good at abstract features from massive data and has good generalization ability,which has attracted more and more researchers’ attention. The Convolutional Neural Network（ CNN） is a classic structure of deep learing and which is being widely and successfully used in the fields of computer vision,target detection,natural language processing,and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis,this paper introduces the structure of CNN,and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type,network structure design and migration learning. The problems of deep feature extraction and visualization are also discussed,and finally,the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN are prospected.

THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

WU DingHai / REN GuoQuan / WANG HuaiGuang / ZHANG YunQiang

Deep learning is good at abstract features from massive data and has good generalization ability,which has attracted more and more researchers’ attention. The Convolutional Neural Network（ CNN） is a classic structure of deep learing and which is being widely and successfully used in the fields of computer vision,target detection,natural language processing,and speech recognition. Based on a detailed analysis of the current status and needs of mechanical system fault diagnosis,this paper introduces the structure of CNN,and summarizes the application of CNN in the field of mechanical faults from the aspects of input data type,network structure design and migration learning. The problems of deep feature extraction and visualization are also discussed,and finally,the difficulties in mechanical fault diagnosis are analyzed and several problems to be solved in the field of mechanical fault diagnosis based on CNN are prospected.

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Titel:

THE REVIEW OF MECHANICAL FAULT DIAGNOSIS METHODS BASED ON CONVOLUTIONAL NEURAL NETWORK

Beteiligte:

WU DingHai (Autor:in) / REN GuoQuan (Autor:in) / WANG HuaiGuang (Autor:in) / ZHANG YunQiang (Autor:in)

Erschienen in:

Jixie qiangdu, Vol 42, Pp 1024-1032 (2020)

Erscheinungsdatum:

2020

ISSN:

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

Schlagwörter:

Deep learning , Convolutional neural network , Big data , Condition monitoring , Fault diagnosis , Transfer learning , Feature extraction , Mechanical engineering and machinery , TJ1-1570 , Materials of engineering and construction. Mechanics of materials , TA401-492

Metadata by DOAJ is licensed under CC BY-SA 1.0

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