Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM
Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonline...
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| Format: | Article |
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/16/5542 |
| _version_ | 1827710452786790400 |
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| author | Rui Li Chao Ran Bin Zhang Leng Han Song Feng |
| author_facet | Rui Li Chao Ran Bin Zhang Leng Han Song Feng |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches. |
| first_indexed | 2024-03-10T17:39:18Z |
| format | Article |
| id | doaj.art-06748e38c9d34cb7849949ec66dd3d0a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:39:18Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-06748e38c9d34cb7849949ec66dd3d0a2023-11-20T09:44:55ZengMDPI AGApplied Sciences2076-34172020-08-011016554210.3390/app10165542Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVMRui Li0Chao Ran1Bin Zhang2Leng Han3Song Feng4School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaRolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.https://www.mdpi.com/2076-3417/10/16/5542fault diagnosisrolling bearingnonlinear entropyimproved complete ensemble empirical mode decomposition with adaptive noiseensemble SVMrotating machines |
| spellingShingle | Rui Li Chao Ran Bin Zhang Leng Han Song Feng Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM Applied Sciences fault diagnosis rolling bearing nonlinear entropy improved complete ensemble empirical mode decomposition with adaptive noise ensemble SVM rotating machines |
| title | Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM |
| title_full | Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM |
| title_fullStr | Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM |
| title_full_unstemmed | Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM |
| title_short | Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM |
| title_sort | rolling bearings fault diagnosis based on improved complete ensemble empirical mode decomposition with adaptive noise nonlinear entropy and ensemble svm |
| topic | fault diagnosis rolling bearing nonlinear entropy improved complete ensemble empirical mode decomposition with adaptive noise ensemble SVM rotating machines |
| url | https://www.mdpi.com/2076-3417/10/16/5542 |
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