Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis
Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and lef...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-04-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/19/4/176 |
| _version_ | 1797999507255853056 |
|---|---|
| author | Yangde Gao Francesco Villecco Ming Li Wanqing Song |
| author_facet | Yangde Gao Francesco Villecco Ming Li Wanqing Song |
| author_sort | Yangde Gao |
| collection | DOAJ |
| description | Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing. |
| first_indexed | 2024-04-11T11:05:45Z |
| format | Article |
| id | doaj.art-dc5e9ceb648b4060b17ebd80366c4129 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:05:45Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-dc5e9ceb648b4060b17ebd80366c41292022-12-22T04:28:21ZengMDPI AGEntropy1099-43002017-04-0119417610.3390/e19040176e19040176Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing DiagnosisYangde Gao0Francesco Villecco1Ming Li2Wanqing Song3Joint Research Lab of Intelligent Perception and Control, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, ChinaDepartment of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, ItalySchool of Information Science and Technology, East China Normal University, Shanghai 200241, ChinaJoint Research Lab of Intelligent Perception and Control, School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, ChinaBased on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.http://www.mdpi.com/1099-4300/19/4/176improved LMDmulti-scale permutation entropyMIFNNdelay timeembedding dimensionHMMback-propagation (BP)bearing fault diagnosis |
| spellingShingle | Yangde Gao Francesco Villecco Ming Li Wanqing Song Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis Entropy improved LMD multi-scale permutation entropy MI FNN delay time embedding dimension HMM back-propagation (BP) bearing fault diagnosis |
| title | Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis |
| title_full | Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis |
| title_fullStr | Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis |
| title_full_unstemmed | Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis |
| title_short | Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis |
| title_sort | multi scale permutation entropy based on improved lmd and hmm for rolling bearing diagnosis |
| topic | improved LMD multi-scale permutation entropy MI FNN delay time embedding dimension HMM back-propagation (BP) bearing fault diagnosis |
| url | http://www.mdpi.com/1099-4300/19/4/176 |
| work_keys_str_mv | AT yangdegao multiscalepermutationentropybasedonimprovedlmdandhmmforrollingbearingdiagnosis AT francescovillecco multiscalepermutationentropybasedonimprovedlmdandhmmforrollingbearingdiagnosis AT mingli multiscalepermutationentropybasedonimprovedlmdandhmmforrollingbearingdiagnosis AT wanqingsong multiscalepermutationentropybasedonimprovedlmdandhmmforrollingbearingdiagnosis |