Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy
In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subban...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/4805383 |
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| _version_ | 1832567927310647296 |
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| author | Lei Zhang Long Zhang Junfeng Hu Guoliang Xiong |
| author_facet | Lei Zhang Long Zhang Junfeng Hu Guoliang Xiong |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier. |
| format | Article |
| id | doaj-art-36b3402d5dbf4fefbeb2817b66da072c |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-36b3402d5dbf4fefbeb2817b66da072c2025-02-03T01:00:03ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/48053834805383Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample EntropyLei Zhang0Long Zhang1Junfeng Hu2Guoliang Xiong3School of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechatronics Engineering, East China Jiaotong University, Nanchang 330013, ChinaIn order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.http://dx.doi.org/10.1155/2016/4805383 |
| spellingShingle | Lei Zhang Long Zhang Junfeng Hu Guoliang Xiong Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy Shock and Vibration |
| title | Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy |
| title_full | Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy |
| title_fullStr | Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy |
| title_full_unstemmed | Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy |
| title_short | Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy |
| title_sort | bearing fault diagnosis using a novel classifier ensemble based on lifting wavelet packet transforms and sample entropy |
| url | http://dx.doi.org/10.1155/2016/4805383 |
| work_keys_str_mv | AT leizhang bearingfaultdiagnosisusinganovelclassifierensemblebasedonliftingwaveletpackettransformsandsampleentropy AT longzhang bearingfaultdiagnosisusinganovelclassifierensemblebasedonliftingwaveletpackettransformsandsampleentropy AT junfenghu bearingfaultdiagnosisusinganovelclassifierensemblebasedonliftingwaveletpackettransformsandsampleentropy AT guoliangxiong bearingfaultdiagnosisusinganovelclassifierensemblebasedonliftingwaveletpackettransformsandsampleentropy |