A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on t...
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| Published in: | IEEE transactions on industrial electronics (1982) Vol. 65; no. 7; pp. 5990 - 5998 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0278-0046, 1557-9948 |
| Online Access: | Get full text |
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| Abstract | Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements. |
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| AbstractList | Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements. |
| Author | Long Wen Liang Gao Yuyan Zhang Xinyu Li |
| Author_xml | – sequence: 1 givenname: Long orcidid: 0000-0002-8355-9947 surname: Wen fullname: Wen, Long – sequence: 2 givenname: Xinyu orcidid: 0000-0002-3629-9193 surname: Li fullname: Li, Xinyu – sequence: 3 givenname: Liang orcidid: 0000-0002-1485-0722 surname: Gao fullname: Gao, Liang – sequence: 4 givenname: Yuyan orcidid: 0000-0003-3375-3482 surname: Zhang fullname: Zhang, Yuyan |
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| CODEN | ITIED6 |
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| SubjectTerms | Adaptive filters Artificial neural networks Axial flow pumps Belief networks Centrifugal pumps Convolutional neural network (CNN) data-driven Datasets Fault diagnosis Feature extraction Image classification Machine learning Monitoring Neural networks Priming Support vector machines Time-domain analysis |
| Title | A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method |
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