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• W2007683166 abstract "This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals." @default.
• W2007683166 created "2016-06-24" @default.
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• W2007683166 date "2008-02-13" @default.
• W2007683166 modified "2023-10-18" @default.
• W2007683166 title "Machine fault feature extraction based on intrinsic mode functions" @default.
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• W2007683166 doi "https://doi.org/10.1088/0957-0233/19/4/045105" @default.
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