A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis
L Cui, Z Jiang, D Liu, H Wang - Expert Systems with Applications, 2024 - Elsevier
Dictionary learning has gradually attracted attention due to its powerful feature
representation ability. However, the time-shift property of collected signals hinders the …
representation ability. However, the time-shift property of collected signals hinders the …
High-fidelity fault signature extraction of rolling bearings via nonconvex regularized sparse representation enhanced by flexible analytical wavelet transform
C Zhang, Y Qiang, W Hou, K Cai… - Structural Health …, 2024 - journals.sagepub.com
Diagnosing the bearing fault, especially incipient fault is important for equipment health
management while is still a challenge in which high-fidelity extraction of the fault signature is …
management while is still a challenge in which high-fidelity extraction of the fault signature is …
Separation of fault characteristic impulses of flexible thin-wall bearing based on wavelet transform and correlated Gini index
Y Yu, X Zhao - Mechanical Systems and Signal Processing, 2024 - Elsevier
The flexible thin-wall bearing, characterized by different kinematic attributes and fault
characteristic frequency in comparison to rolling bearings, introduces a great challenge in …
characteristic frequency in comparison to rolling bearings, introduces a great challenge in …
A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions
Rotating machinery (RM) such as bearings and gears often operates under time-varying
operating conditions (TVOC), which makes the vibration signals non-stationary. In this case …
operating conditions (TVOC), which makes the vibration signals non-stationary. In this case …
Bearing fault-induced feature enhancement via adaptive multi-band denoising model
L Zhao, L Zhang, H Zhang, Y Hu - Measurement Science and …, 2023 - iopscience.iop.org
To accurately extract the bearing fault-induced impulse features from the vibration signals
corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band …
corrupted by heavy noise and large-amplitude random impulses, an adaptive multi-band …
A novel incipient fault diagnosis method for analogue circuits based on an MLDLCN
X Liu, H Yang, T Gao, J Yang - Circuits, Systems, and Signal Processing, 2024 - Springer
Incipient faults in analogue circuits used in complex electrical systems are hard to diagnose
due to weak fault features. To improve the reliability and maintainability of analogue circuits …
due to weak fault features. To improve the reliability and maintainability of analogue circuits …
Deep discriminative sparse representation learning for machinery fault diagnosis
R Yao, H Jiang, W Jiang, Y Liu, Y Dong - Engineering Applications of …, 2024 - Elsevier
The high complexity of actual machinery vibration environments introduces various
interferences into vibration signals, making it challenging to eliminate redundant information …
interferences into vibration signals, making it challenging to eliminate redundant information …
Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling
It is an effective means to improve the machining quality of product by monitoring the tool
wear conditions in micro milling. However, due to the small size and high speed of the micro …
wear conditions in micro milling. However, due to the small size and high speed of the micro …
Structured collaborative sparse dictionary learning for monitoring of multimode processes
In this paper, a novel structured collaborative sparse dictionary learning approach is
proposed to improve the monitoring performance of discriminative dictionary learning for …
proposed to improve the monitoring performance of discriminative dictionary learning for …
[HTML][HTML] Unsupervised complex semi-binary matrix factorization for activation sequence recovery of quasi-stationary sources
Industry 5.0 advocates for a sustainable industry, particularly in terms of energy. One of the
most fundamental elements of comprehension is knowing when the systems are operated …
most fundamental elements of comprehension is knowing when the systems are operated …