An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing
Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/354 |
| _version_ | 1798005575697563648 |
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| author | Shuting Wan Bo Peng |
| author_facet | Shuting Wan Bo Peng |
| author_sort | Shuting Wan |
| collection | DOAJ |
| description | Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods. |
| first_indexed | 2024-04-11T12:42:30Z |
| format | Article |
| id | doaj.art-cdddfcbf6e944dd3afbb3b661842928a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T12:42:30Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-cdddfcbf6e944dd3afbb3b661842928a2022-12-22T04:23:28ZengMDPI AGEntropy1099-43002019-04-0121435410.3390/e21040354e21040354An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling BearingShuting Wan0Bo Peng1Department of Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaDepartment of Mechanical Engineering, North China Electric Power University, Baoding 071000, ChinaAiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.https://www.mdpi.com/1099-4300/21/4/354swarm decompositionmorphology envelope dispersion entropyrandom forestmulti-fault recognitionrolling bearing |
| spellingShingle | Shuting Wan Bo Peng An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing Entropy swarm decomposition morphology envelope dispersion entropy random forest multi-fault recognition rolling bearing |
| title | An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing |
| title_full | An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing |
| title_fullStr | An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing |
| title_full_unstemmed | An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing |
| title_short | An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing |
| title_sort | integrated approach based on swarm decomposition morphology envelope dispersion entropy and random forest for multi fault recognition of rolling bearing |
| topic | swarm decomposition morphology envelope dispersion entropy random forest multi-fault recognition rolling bearing |
| url | https://www.mdpi.com/1099-4300/21/4/354 |
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