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• W2945329120 abstract "According to the nonlinearity and nonstationarity characteristics of reciprocating compressor vibration signal, a fault feature extraction method of reciprocating compressor based on the empirical wavelet transform (EWT) and state-adaptive morphological filtering (SMF) is proposed. Firstly, an adaptive empirical wavelet transform was used to divide the Fourier spectrum by constructing a scale-space curve, and an appropriate orthogonal wavelet filter bank was constructed to extract the AM-FM component with a tightly-supported Fourier spectrum. Then according to the impact characteristic of the reciprocating compressor vibration signal, the morphological structural elements were constructed with the characteristics of the signal to perform state-adaptive morphological filtering on the partitioned modal functions. Finally, the MF-DFA method of the modal function was quantitatively analyzed and the fault identification was performed. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve." @default.
• W2945329120 created "2019-05-29" @default.
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• W2945329120 date "2019-05-15" @default.
• W2945329120 modified "2023-10-14" @default.
• W2945329120 title "Fault feature extraction method based on EWT-SMF and MF-DFA for valve fault of reciprocating compressor" @default.
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• W2945329120 doi "https://doi.org/10.21595/jve.2018.20050" @default.
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