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- 저자 : Guiji Tang (검색결과 1 건)
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Guiji Tang,Xiaolong Wang,Yuling He 대한기계학회 2016 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.30 No.1
This paper proposes a new diagnosis method based on Adaptive maximum correlated kurtosis deconvolution (AMCKD) for accurateidentification of compound faults of rolling bearings. The AMCKD method combines the powerful capability of cuckoo search algorithmfor global optimization with the advantage of Maximum correlated kurtosis deconvolution (MCKD) for impact signal extraction. In contrastto traditional methods, such as direct envelop spectrum, Discrete wavelet transform (DWT), and empirical mode decomposition, theproposed method extracts each fault signal related to the single failed part from the compound fault signals and effectively separates thecoupled fault features. First, the original signal is processed using AMCKD method. Demodulation operation is then performed on theobtained single fault signal, and the envelope spectrum is calculated to identify the characteristic frequency information. Verification isperformed on simulated and experimental signals. Results show that the proposed method is more suitable for detecting compound faultsin rolling bearings compared with traditional methods. This research provides a basis for improving the monitoring and diagnosis precisionof rolling bearings.
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