Semi-supervised contrast learning based on multiscale attention and multitarget contrast learning for bearing fault diagnosis
W Zhang, D Chen, Y **ao, H Yin - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In the field of bearing fault diagnosis, it is difficult to obtain a large amount of labeled data for
training, and therefore it is easy to overfit the model, which makes it less robust and less …
training, and therefore it is easy to overfit the model, which makes it less robust and less …
A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery
W Li, Z Shang, M Gao, S Qian, B Zhang… - … Applications of Artificial …, 2021 - Elsevier
Relying on the purpose of transmitting force and torque, rotating machinery is widely used in
various industrial equipment. The failure of rotating machinery leads to large maintenance …
various industrial equipment. The failure of rotating machinery leads to large maintenance …
Feature extraction of rolling bearing multiple faults based on correlation coefficient and Hjorth parameter
M Yu, M Fang - ISA transactions, 2022 - Elsevier
The paper has brought in the Hjorth Complexity parameter and combined it with Intrinsic
time decomposition (ITD) algorithm as characteristic parameter index in order to implement …
time decomposition (ITD) algorithm as characteristic parameter index in order to implement …
Detectivity: A combination of Hjorth's parameters for condition monitoring of ball bearings
Hjorth's parameters are statistical time-domain parameters used in signal processing and
introduced by Bo Hjorth in 1970. These parameters are Activity, Mobility and Complexity …
introduced by Bo Hjorth in 1970. These parameters are Activity, Mobility and Complexity …
Bearing fault-detection method based on improved grey wolf algorithm to optimize parameters of multistable stochastic resonance
W Huang, G Zhang - Sensors, 2023 - mdpi.com
In an effort to overcome the problem that the traditional stochastic resonance system cannot
adjust the structural parameters adaptively in bearing fault-signal detection, this article …
adjust the structural parameters adaptively in bearing fault-signal detection, this article …
Feature extraction for early fault detection in rotating machinery of nuclear power plants based on adaptive VMD and Teager energy operator
S Zhu, H **a, B Peng, E Zio, Z Wang, Y Jiang - Annals of Nuclear Energy, 2021 - Elsevier
Extracting features for early failure detection in rotating machinery of nuclear power plants
(NPPs) is difficult because in the early stages of failure the impact on the vibration signals is …
(NPPs) is difficult because in the early stages of failure the impact on the vibration signals is …
[HTML][HTML] Towards emotionally intelligent virtual environments: classifying emotions through a biosignal-based approach
This paper introduces a novel method for emotion classification within virtual reality (VR)
environments, which integrates biosignal processing with advanced machine learning …
environments, which integrates biosignal processing with advanced machine learning …
Fault diagnosis and location identification of rotor–stator rub-impact based on Hjorth parameters
M Yu, W Chen, Y Lu - Engineering Failure Analysis, 2022 - Elsevier
Abstract Hjorth parameters reveals Activity, Mobility and Complexity of signals. From this, the
paper has proposed the scheme to identify a rotor–stator rub-impact fault and occurrence …
paper has proposed the scheme to identify a rotor–stator rub-impact fault and occurrence …
Accurate detection of bearing faults using difference visibility graph and bi-directional long short-term memory network classifier
This article proposes a novel bearing fault detection framework for the real-time condition
monitoring of induction motors based on difference visibility graph (DVG) theory. In this …
monitoring of induction motors based on difference visibility graph (DVG) theory. In this …
A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy
The vibration signals of rolling bearings are complex and changeable, and extracting
meaningful features is difficult. Currently, the commonly used empirical mode decomposition …
meaningful features is difficult. Currently, the commonly used empirical mode decomposition …