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• W2762681924 abstract "Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) on fault diagnosis of rotating shafts using multiscale entropy (MSE). After WPD and EMD, the most sensitive reconstruction vectors and intrinsic mode functions (IMFs) are selected using Shannon entropy. Then, these feature vectors are fed into support vector machine (SVM) for fault classification, where the entropy features represent the complexity of vibration signals with different scales. Experimental results have demonstrated that WPD combined with MSE can achieve an accuracy of 97.3% for crack fault detection in rotating shafts, whilst EMD combined with MSE has shown a higher detection rate of 98.5%." @default.
• W2762681924 created "2017-10-20" @default.
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• W2762681924 date "2017-10-01" @default.
• W2762681924 modified "2023-09-22" @default.
• W2762681924 title "A comparative study of WPD and EMD for shaft fault diagnosis" @default.
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• W2762681924 doi "https://doi.org/10.1109/iecon.2017.8217482" @default.
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