Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis

In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting fro...

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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 70; H. 2; S. 1949 - 1960
Hauptverfasser: Miao, Yonghao, Zhang, Boyao, Li, Chenhui, Lin, Jing, Zhang, Dayi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive filter
correlated Kurtosis (CK)
Decomposition
Deconvolution
Fault diagnosis
Feature extraction
feature mode decomposition (FMD)
Filter banks
Filtering theory
Finite impulse response filters
FIR filters
Impulse response
Kurtosis
Machinery
machinery fault diagnosis
Modal choice
Periodic variations
Rotating machinery
Wiener filters
ISSN:0278-0046, 1557-9948
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Zusammenfassung:In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose of decomposing the different modes by the designed adaptive finite-impulse response (FIR) filters. Benefitting from the superiority of correlated Kurtosis, FMD takes the impulsiveness and periodicity of fault signal into consideration simultaneously. First, a designed FIR filter bank by Hanning window initialization is used to provide a direction for the decomposition. The period estimation and updating process are then used to lock the fault information. Finally, the redundant and mixing modes are removed in the process of mode selection. The superiority of the FMD is demonstrated to adaptively and accurately decompose the fault mode as well as robust to other interferences and noise using simulated and experimental data collected from bearing single and compound fault. Moreover, it has been demonstrated that FMD has superiority in feature extraction of machinery fault compared with the most popular variational mode decomposition.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3156156