Fengshou Gu
- : Performance of vibration and current signals in the fault diagnosis of induction motors using deep learning and machine learning techniques. Dans: Structural Health Monitoring.
- : An unsupervised transfer network with adaptive input and dynamic channel pruning for train axle bearing fault diagnosis. Dans: Structural Health Monitoring.
- (2023): Maximum negative entropy deconvolution and its application to bearing condition monitoring. Dans: Structural Health Monitoring, v. 23, n. 3 (septembre 2023).
- (2023): Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics. Dans: Structural Health Monitoring, v. 22, n. 6 (avril 2023).
- (2023): Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring. Dans: Structural Health Monitoring, v. 22, n. 6 (avril 2023).
- (2023): A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis. Dans: Structural Health Monitoring, v. 22, n. 5 (février 2023).
- (2019): A Modulation Signal Bispectrum Enhanced Squared Envelope for the detection and diagnosis of compound epicyclic gear faults. Dans: Structural Health Monitoring, v. 22, n. 1 (novembre 2019).
- (2019): An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis. Dans: Structural Health Monitoring, v. 22, n. 1 (novembre 2019).
- (2021): Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis. Dans: Structural Health Monitoring, v. 21, n. 3 (août 2021).
- (2021): A phase linearisation–based modulation signal bispectrum for analysing cyclostationary bearing signals. Dans: Structural Health Monitoring, v. 20, n. 3 (janvier 2021).
Structurae coopère avec
