Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
Nonnegative Tucker3 decomposition(NTD)has attracted lots of attentions for its good performance in 3D data array analysis.However,further research is still necessary to solve the problems of overfitting and slow conve...Nonnegative Tucker3 decomposition(NTD)has attracted lots of attentions for its good performance in 3D data array analysis.However,further research is still necessary to solve the problems of overfitting and slow convergence under the anharmonic vibration circumstance occurred in the field of mechanical fault diagnosis.To decompose a large-scale tensor and extract available bispectrum feature,a method of conjugating Choi-Williams kernel function with Gauss-Newton Cartesian product based on nonnegative Tucker3 decomposition(NTD_EDF)is investigated.The complexity of the proposed method is reduced from o(nNlgn)in 3D spaces to o(RiR2nlgn)in 1D vectors due to its low rank form of the Tucker-product convolution.Meanwhile,a simultaneously updating algorithm is given to overcome the overfitting,slow convergence and low efficiency existing in the conventional one-by-one updating algorithm.Furthermore,the technique of spectral phase analysis for quadratic coupling estimation is used to explain the feature spectrum extracted from the gearbox fault data by the proposed method in detail.The simulated and experimental results show that the sparser and more inerratic feature distribution of basis images can be obtained with core tensor by the NTD EDF method compared with the one by the other methods in bispectrum feature extraction,and a legible fault expression can also be performed by power spectral density(PSD)function.Besides,the deviations of successive relative error(DSRE)of NTD_EDF achieves 81.66 dB against 15.17 dB by beta-divergences based on NTD(NTD_Beta)and the time-cost of NTD EDF is only 129.3 s,which is far less than 1747.9 s by hierarchical alternative least square based on NTD(NTD_HALS).The NTD_EDF method proposed not only avoids the data overfitting and improves the computation efficiency but also can be used to extract more inerratic and sparser bispectrum features of the gearbox fault.展开更多
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金supported by National Natural Science Foundation of China(Grant Nos.50875048,51175079,51075069)
文摘Nonnegative Tucker3 decomposition(NTD)has attracted lots of attentions for its good performance in 3D data array analysis.However,further research is still necessary to solve the problems of overfitting and slow convergence under the anharmonic vibration circumstance occurred in the field of mechanical fault diagnosis.To decompose a large-scale tensor and extract available bispectrum feature,a method of conjugating Choi-Williams kernel function with Gauss-Newton Cartesian product based on nonnegative Tucker3 decomposition(NTD_EDF)is investigated.The complexity of the proposed method is reduced from o(nNlgn)in 3D spaces to o(RiR2nlgn)in 1D vectors due to its low rank form of the Tucker-product convolution.Meanwhile,a simultaneously updating algorithm is given to overcome the overfitting,slow convergence and low efficiency existing in the conventional one-by-one updating algorithm.Furthermore,the technique of spectral phase analysis for quadratic coupling estimation is used to explain the feature spectrum extracted from the gearbox fault data by the proposed method in detail.The simulated and experimental results show that the sparser and more inerratic feature distribution of basis images can be obtained with core tensor by the NTD EDF method compared with the one by the other methods in bispectrum feature extraction,and a legible fault expression can also be performed by power spectral density(PSD)function.Besides,the deviations of successive relative error(DSRE)of NTD_EDF achieves 81.66 dB against 15.17 dB by beta-divergences based on NTD(NTD_Beta)and the time-cost of NTD EDF is only 129.3 s,which is far less than 1747.9 s by hierarchical alternative least square based on NTD(NTD_HALS).The NTD_EDF method proposed not only avoids the data overfitting and improves the computation efficiency but also can be used to extract more inerratic and sparser bispectrum features of the gearbox fault.
