An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis
Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault dia...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/8/1729 |
| _version_ | 1818013402948173824 |
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| author | Shaobo Li Guokai Liu Xianghong Tang Jianguang Lu Jianjun Hu |
| author_facet | Shaobo Li Guokai Liu Xianghong Tang Jianguang Lu Jianjun Hu |
| author_sort | Shaobo Li |
| collection | DOAJ |
| description | Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. |
| first_indexed | 2024-04-14T06:33:49Z |
| format | Article |
| id | doaj.art-9cfc1e96027a4bd987aad6cbd9739384 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:33:49Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-9cfc1e96027a4bd987aad6cbd97393842022-12-22T02:07:33ZengMDPI AGSensors1424-82202017-07-01178172910.3390/s17081729s17081729An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault DiagnosisShaobo Li0Guokai Liu1Xianghong Tang2Jianguang Lu3Jianjun Hu4Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaKey Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, ChinaSchool of Mechanical Engineering, Guizhou University, Guiyang 550025, ChinaIntelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.https://www.mdpi.com/1424-8220/17/8/1729bearing fault diagnosisD-S evidence theoryconvolutional neural networksdeep learning |
| spellingShingle | Shaobo Li Guokai Liu Xianghong Tang Jianguang Lu Jianjun Hu An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis Sensors bearing fault diagnosis D-S evidence theory convolutional neural networks deep learning |
| title | An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis |
| title_full | An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis |
| title_fullStr | An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis |
| title_full_unstemmed | An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis |
| title_short | An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis |
| title_sort | ensemble deep convolutional neural network model with improved d s evidence fusion for bearing fault diagnosis |
| topic | bearing fault diagnosis D-S evidence theory convolutional neural networks deep learning |
| url | https://www.mdpi.com/1424-8220/17/8/1729 |
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