An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering
In order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firs...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/1507630 |
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| _version_ | 1832553586113904640 |
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| author | Hongping Ge Zhaohui Huang Huaying Liu |
| author_facet | Hongping Ge Zhaohui Huang Huaying Liu |
| author_sort | Hongping Ge |
| collection | DOAJ |
| description | In order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firstly, the smoothness priors approach is used to decompose the collected vibration signal of rotating machinery to obtain the trend term and detrend term. Secondly, the fault features of the trend term and detrend term were quantified by fluctuation dispersion entropy to construct eigenvector matrix. Finally, the eigenvector matrix was input into the density peak clustering algorithm for fault recognition and classification. The proposed novel algorithm was applied to the experimental data of the rotating machinery under various working conditions. The experimental results show that our method can precisely identify various fault patterns of rotating machinery. Moreover, our approach can attain higher recognition accuracy than other combination clustering model algorithms involved in this paper. |
| format | Article |
| id | doaj-art-0da946f6f9b7449d83e27da57cac682c |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-0da946f6f9b7449d83e27da57cac682c2025-02-03T05:53:39ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/1507630An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak ClusteringHongping Ge0Zhaohui Huang1Huaying Liu2Science and Technology College of Nanchang Hangkong UniversityScience and Technology College of Nanchang Hangkong UniversityScience and Technology College of Nanchang Hangkong UniversityIn order to fully excavate the fault feature information of rotating machinery and accurately recognize the fault category, a novel fault diagnosis method was proposed, which combines with smoothness priors approach (SPA), fluctuation dispersion entropy (FDE), and density peak clustering (DPC). Firstly, the smoothness priors approach is used to decompose the collected vibration signal of rotating machinery to obtain the trend term and detrend term. Secondly, the fault features of the trend term and detrend term were quantified by fluctuation dispersion entropy to construct eigenvector matrix. Finally, the eigenvector matrix was input into the density peak clustering algorithm for fault recognition and classification. The proposed novel algorithm was applied to the experimental data of the rotating machinery under various working conditions. The experimental results show that our method can precisely identify various fault patterns of rotating machinery. Moreover, our approach can attain higher recognition accuracy than other combination clustering model algorithms involved in this paper.http://dx.doi.org/10.1155/2022/1507630 |
| spellingShingle | Hongping Ge Zhaohui Huang Huaying Liu An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering Shock and Vibration |
| title | An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering |
| title_full | An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering |
| title_fullStr | An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering |
| title_full_unstemmed | An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering |
| title_short | An Integrated Fault Diagnosis Method for Rotating Machinery Based on Smoothness Priors Approach Fluctuation Dispersion Entropy and Density Peak Clustering |
| title_sort | integrated fault diagnosis method for rotating machinery based on smoothness priors approach fluctuation dispersion entropy and density peak clustering |
| url | http://dx.doi.org/10.1155/2022/1507630 |
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