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• W2091259739 abstract "Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults." @default.
• W2091259739 created "2016-06-24" @default.
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• W2091259739 date "2011-07-06" @default.
• W2091259739 modified "2023-10-16" @default.
• W2091259739 title "Application of S transform and morphological pattern spectrum for gear fault diagnosis" @default.
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• W2091259739 doi "https://doi.org/10.1177/0954406211408781" @default.
• W2091259739 hasPublicationYear "2011" @default.
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