An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis
The performance of classifiers plays a crucial role in identifying fault categories under mechanical fault diagnosis task. This paper presents a new intelligent classification approach via NA-MEMD and multiple classifier fusion for fault diagnosis, which comprises four stages. First, NA-MEMD extract...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10261209/ |
| _version_ | 1797668016728571904 |
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| author | Lin Li Shiyan Sun Yaqin Zeng Zhangsong Shi Xuan Wang |
| author_facet | Lin Li Shiyan Sun Yaqin Zeng Zhangsong Shi Xuan Wang |
| author_sort | Lin Li |
| collection | DOAJ |
| description | The performance of classifiers plays a crucial role in identifying fault categories under mechanical fault diagnosis task. This paper presents a new intelligent classification approach via NA-MEMD and multiple classifier fusion for fault diagnosis, which comprises four stages. First, NA-MEMD extracts time-domain and frequency-domain features from the original vibration signal. Second, Sensitive feature extraction using improved Fisher’s criterion method. Third, the sensitive feature sets (SFS) are input to Depth Gaussian Restricted Boltzmann Machine (DG-RBM), RNN, and CNN classification methods to attain the complementary benefits and substantial fusion of various classifiers. Final, the Bayesian belief approach is utilized for fusing the classification results of multiple classifiers to obtain the diagnosis results. Experiments on rolling bearing datasets reveal that the presented approach can precisely detect the fault conditions and provide a classification efficiency superior to the single classifiers and ensemble of all classifiers. The experimental results have revealed the efficiency, generalization, and robustness of the multiple classifier fusion. |
| first_indexed | 2024-03-11T20:21:57Z |
| format | Article |
| id | doaj.art-04dfe86582d945ad8799fb10392b89c2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T20:21:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-04dfe86582d945ad8799fb10392b89c22023-10-02T23:01:30ZengIEEEIEEE Access2169-35362023-01-011110504010505610.1109/ACCESS.2023.331831910261209An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault DiagnosisLin Li0https://orcid.org/0009-0004-7257-6639Shiyan Sun1Yaqin Zeng2Zhangsong Shi3Xuan Wang4College of Weapon Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Weapon Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Weapon Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Weapon Engineering, Naval University of Engineering, Wuhan, ChinaCollege of Weapon Engineering, Naval University of Engineering, Wuhan, ChinaThe performance of classifiers plays a crucial role in identifying fault categories under mechanical fault diagnosis task. This paper presents a new intelligent classification approach via NA-MEMD and multiple classifier fusion for fault diagnosis, which comprises four stages. First, NA-MEMD extracts time-domain and frequency-domain features from the original vibration signal. Second, Sensitive feature extraction using improved Fisher’s criterion method. Third, the sensitive feature sets (SFS) are input to Depth Gaussian Restricted Boltzmann Machine (DG-RBM), RNN, and CNN classification methods to attain the complementary benefits and substantial fusion of various classifiers. Final, the Bayesian belief approach is utilized for fusing the classification results of multiple classifiers to obtain the diagnosis results. Experiments on rolling bearing datasets reveal that the presented approach can precisely detect the fault conditions and provide a classification efficiency superior to the single classifiers and ensemble of all classifiers. The experimental results have revealed the efficiency, generalization, and robustness of the multiple classifier fusion.https://ieeexplore.ieee.org/document/10261209/Multiple classifier fusionNA-MEMDFisher's criterionBayesian belief method |
| spellingShingle | Lin Li Shiyan Sun Yaqin Zeng Zhangsong Shi Xuan Wang An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis IEEE Access Multiple classifier fusion NA-MEMD Fisher's criterion Bayesian belief method |
| title | An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis |
| title_full | An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis |
| title_fullStr | An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis |
| title_full_unstemmed | An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis |
| title_short | An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis |
| title_sort | intelligent classification approach via multiple classifier fusion and its application to the fault diagnosis |
| topic | Multiple classifier fusion NA-MEMD Fisher's criterion Bayesian belief method |
| url | https://ieeexplore.ieee.org/document/10261209/ |
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