Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads

M Qiao, S Yan, X Tang, C Xu - Ieee Access, 2020 - ieeexplore.ieee.org
To research the problems of the rolling bearing fault diagnosis under different noises and
loads, a dual-input model based on a convolutional neural network (CNN) and long-short …

Bearing weak fault feature extraction under time-varying speed conditions based on frequency matching demodulation transform

D Zhao, L Cui, D Liu - IEEE/ASME Transactions on …, 2022 - ieeexplore.ieee.org
Bearing weak fault feature extraction under time-varying speed conditions is a challenging
task. The classic time-frequency analysis (TFA) based ridge detection algorithms cannot …

Generalized broadband mode decomposition method and its application in fault diagnosis of variable speed spherical roller bearing

H Geng, Y Peng, L Ye, Y Guo - Measurement, 2023 - Elsevier
Fault signals of roller bearing with variable speed often present non-stationary
characteristics which are difficult to be accurately extracted by existing analytical methods …

Development of an embedded piezoelectric transducer for bearing fault detection

A Safian, N Wu, X Liang - Mechanical Systems and Signal Processing, 2023 - Elsevier
The trend toward intelligent monitoring and industry 4.0 has attracted more attention to the
performance of intelligent bearings with integrated sensors. Compared with the standard …

Fault diagnosis of wind turbine bearing based on optimized adaptive chirp mode decomposition

X Wang, G Tang, X Yan, Y He, X Zhang… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
Periodic impacts are considered as the important defect signatures of wind turbine bearing.
However, it is difficult to separate the weak periodic impacts from collected signal under …

Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction

D Zhao, T Wang, RX Gao, F Chu - Mechanical Systems and Signal …, 2019 - Elsevier
In this paper, a novel signal optimization based generalized demodulation transform
(SOGDT) is proposed for rolling bearing nonstationary fault characteristic extraction. This …

Fault identification of rolling bearing with variable speed based on generalized broadband mode decomposition and distance evaluation technique

H Geng, Y Peng, L Ye, Y Guo - Digital Signal Processing, 2022 - Elsevier
In the fault diagnosis of rolling bearing, fault signals are often interfered by other noise
signals, so that the fault characteristics are not obvious. In particular, vibration signals of …

Deep spatiotemporal convolutional-neural-network-based remaining useful life estimation of bearings

X Wang, T Wang, A Ming, Q Han, F Chu… - Chinese Journal of …, 2021 - Springer
The remaining useful life (RUL) estimation of bearings is critical for ensuring the reliability of
mechanical systems. Owing to the rapid development of deep learning methods, a multitude …

K-SVD-based WVD enhancement algorithm for planetary gearbox fault diagnosis under a CNN framework

H Li, Q Zhang, X Qin, Y Sun - Measurement Science and …, 2019 - iopscience.iop.org
This paper presents a new method of planetary gearbox fault diagnosis by dealing with and
analyzing vibration signals. This study contributes to the realization of automatic diagnosis …

Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique

D Zhao, L Gelman, F Chu, A Ball - Structural Control and Health …, 2021 - Wiley Online Library
Time‐varying fault impulse amplitude and time‐varying fault impulse interval are the main
challenges for rolling bearing fault diagnosis under variable speed conditions. In this paper …