It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of th...It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of the data,or the conclusions reached.The authors would like to apologize for any inconvenience caused.展开更多
A fault identification method ofrotating machinery is proposed,which combines wavelet packet of time-frequency analysis and manifold learning.Firstly,the sampled vibration signal is decomposed to multilayer informatio...A fault identification method ofrotating machinery is proposed,which combines wavelet packet of time-frequency analysis and manifold learning.Firstly,the sampled vibration signal is decomposed to multilayer information with wavelet packet decomposition(WPD) method.Andevery level data of wavelet packet decomposition is processed bydemodulatingof Hilbert transform,eliminating the high frequency noiseof FIR filterand reducing the data length of the low frequency of resampling.Further,every level data vector is deal with normalization and calculated for the auto power spectrum.Finally,the manifold learning methods of t distributed stochastic neighbor embedding(t-SNE) is applied to do dimension reduction to generate 2D manifold figure data.Different fault forms of gearbox have different manifold features,which is used to identify failure status of equipment.With the experiment test,the feasibility and effectiveness of this identification method is verified.展开更多
文摘It is regretful that the data error due to the large number of samples tested.The correct data and figure should be as follows:This correction have no impact on the remainder of the manuscript,the interpretation of the data,or the conclusions reached.The authors would like to apologize for any inconvenience caused.
基金supported by the National Natural Science Foundation-supported Program(515750055)Beijing Municipal Natural Science Foundation(3131002)
文摘A fault identification method ofrotating machinery is proposed,which combines wavelet packet of time-frequency analysis and manifold learning.Firstly,the sampled vibration signal is decomposed to multilayer information with wavelet packet decomposition(WPD) method.Andevery level data of wavelet packet decomposition is processed bydemodulatingof Hilbert transform,eliminating the high frequency noiseof FIR filterand reducing the data length of the low frequency of resampling.Further,every level data vector is deal with normalization and calculated for the auto power spectrum.Finally,the manifold learning methods of t distributed stochastic neighbor embedding(t-SNE) is applied to do dimension reduction to generate 2D manifold figure data.Different fault forms of gearbox have different manifold features,which is used to identify failure status of equipment.With the experiment test,the feasibility and effectiveness of this identification method is verified.
