Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition
The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming...
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/12/16950 |
| _version_ | 1828151837683875840 |
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| author | Yaguo Lei Naipeng Li Jing Lin Sizhe Wang |
| author_facet | Yaguo Lei Naipeng Li Jing Lin Sizhe Wang |
| author_sort | Yaguo Lei |
| collection | DOAJ |
| description | The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery. |
| first_indexed | 2024-04-11T22:03:23Z |
| format | Article |
| id | doaj.art-9beadb7f17b44adaa90b4f7997140ae9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:03:23Z |
| publishDate | 2013-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9beadb7f17b44adaa90b4f7997140ae92022-12-22T04:00:49ZengMDPI AGSensors1424-82202013-12-011312169501696410.3390/s131216950s131216950Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode DecompositionYaguo Lei0Naipeng Li1Jing Lin2Sizhe Wang3State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, ChinaState Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, ChinaThe vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.http://www.mdpi.com/1424-8220/13/12/16950adaptive ensemble empirical mode decompositionfault diagnosissifting numberadded noiserotating machinery |
| spellingShingle | Yaguo Lei Naipeng Li Jing Lin Sizhe Wang Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition Sensors adaptive ensemble empirical mode decomposition fault diagnosis sifting number added noise rotating machinery |
| title | Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition |
| title_full | Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition |
| title_fullStr | Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition |
| title_full_unstemmed | Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition |
| title_short | Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition |
| title_sort | fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition |
| topic | adaptive ensemble empirical mode decomposition fault diagnosis sifting number added noise rotating machinery |
| url | http://www.mdpi.com/1424-8220/13/12/16950 |
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