Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

[1] Sun Y(2021)Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image[J] Measurement 176 109100-184

[2] Li S(2022)Bearing fault diagnosis based on optimal convolution neural network[J] Measurement 190 110702-1953

[3] Wang X(2014)Deep learning and its new progress in object and behavior recognition[J] J Image Grap 19 175-3147

[4] Sun Y(2016)Fault feature extraction and diagnosis method based on deep belief network[J] Chinese J Scient Inst 37 1946-399

[5] Li S(2016)An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J] IEEE Trans Industr Electron 63 3137-3712

[6] Zheng Y(2016)Fault feature detection of rolling bearing based on LMD and third-order cumulant diagonal slice spectrum[J] Appl Mech Mater 851 333-3805

[7] Chen Q(2020)Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering[J] Measurement 152 107293-366

[8] Zhang Y(2021)Graph cardinality preserved attention network for fault diagnosis of induction motor under varying speed and load condition[J] IEEE Trans Industr Inf 18 3702-4971

[9] Zhao G(2021)Fault diagnosis of rolling bearing based on empirical mode decomposition and improved Manhattan distance in symmetrized dot pattern image[J] Mech Syst Signal Proc 159 107817-2389

[10] Ge Q(2021)Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching[J] Measurement 169 108362-189

[1] Sun Y(2021)Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image[J] Measurement 176 109100-184

[2] Li S(2022)Bearing fault diagnosis based on optimal convolution neural network[J] Measurement 190 110702-1953

[3] Wang X(2014)Deep learning and its new progress in object and behavior recognition[J] J Image Grap 19 175-3147

[4] Sun Y(2016)Fault feature extraction and diagnosis method based on deep belief network[J] Chinese J Scient Inst 37 1946-399

[5] Li S(2016)An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J] IEEE Trans Industr Electron 63 3137-3712

[6] Zheng Y(2016)Fault feature detection of rolling bearing based on LMD and third-order cumulant diagonal slice spectrum[J] Appl Mech Mater 851 333-3805

[7] Chen Q(2020)Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering[J] Measurement 152 107293-366

[8] Zhang Y(2021)Graph cardinality preserved attention network for fault diagnosis of induction motor under varying speed and load condition[J] IEEE Trans Industr Inf 18 3702-4971

[9] Zhao G(2021)Fault diagnosis of rolling bearing based on empirical mode decomposition and improved Manhattan distance in symmetrized dot pattern image[J] Mech Syst Signal Proc 159 107817-2389

[10] Ge Q(2021)Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching[J] Measurement 169 108362-189