Comparative Study of Adaptive Signal Decomposition Methods and Their Applications in Locomotive Rolling Element Bearing Fault Diagnosis
In order to extract the fault features effectively, a comparative study of adaptive signal decomposition methods was presented. The local mean computation, decomposed components and decomposition capacity of three conventional methods empirical mode decomposition, local mean decomposition and local...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2017-01-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2017.04.023 |
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| Summary: | In order to extract the fault features effectively, a comparative study of adaptive signal decomposition methods was presented. The local mean computation, decomposed components and decomposition capacity of three conventional methods empirical mode decomposition, local mean decomposition and local characteristic-scale decomposition were analyzed and compared. Aiming at the problems of the local mean decomposition, an improved decomposition algorithm was proposed. The effectiveness of proposed improvements was testified by computer-generated signal. Furthermore, a fault diagnostic approach by jointly using improved LMD and -dimension spectrum was developed. The effectiveness and practicability of the proposed method was verified by DF4 locomotive running tests. |
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| ISSN: | 1000-128X |