A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks
Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some infor...
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| Format: | Article |
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/671 |
| _version_ | 1797490381038813184 |
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| author | Daoguang Yang Hamid Reza Karimi Len Gelman |
| author_facet | Daoguang Yang Hamid Reza Karimi Len Gelman |
| author_sort | Daoguang Yang |
| collection | DOAJ |
| description | Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well. |
| first_indexed | 2024-03-10T00:32:11Z |
| format | Article |
| id | doaj.art-1ba18d028d1e4874bdf3bf6ab97c9d26 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:32:11Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1ba18d028d1e4874bdf3bf6ab97c9d262023-11-23T15:22:32ZengMDPI AGSensors1424-82202022-01-0122267110.3390/s22020671A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural NetworksDaoguang Yang0Hamid Reza Karimi1Len Gelman2Department of Mechnical Engineering, Politecnico di Milano, 20156 Milan, ItalyDepartment of Mechnical Engineering, Politecnico di Milano, 20156 Milan, ItalySchool of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UKSome artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well.https://www.mdpi.com/1424-8220/22/2/671Convolutional Neural Networkrotating machineryfuzzy fusionfault diagnosis |
| spellingShingle | Daoguang Yang Hamid Reza Karimi Len Gelman A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks Sensors Convolutional Neural Network rotating machinery fuzzy fusion fault diagnosis |
| title | A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks |
| title_full | A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks |
| title_fullStr | A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks |
| title_full_unstemmed | A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks |
| title_short | A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks |
| title_sort | fuzzy fusion rotating machinery fault diagnosis framework based on the enhancement deep convolutional neural networks |
| topic | Convolutional Neural Network rotating machinery fuzzy fusion fault diagnosis |
| url | https://www.mdpi.com/1424-8220/22/2/671 |
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