[HTML][HTML] Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Rolling bearings are essential components in a wide range of equipment, such as
aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete …
aeroplanes, trains, and wind turbines. Bearing failure has the potential to result in complete …
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks
The growing complexity of machinery and the increasing demand for operational efficiency
and safety have driven the development of advanced fault diagnosis techniques. Among …
and safety have driven the development of advanced fault diagnosis techniques. Among …
Enhanced fault diagnosis of rolling bearings using an improved inception-lstm network
L Wei, X Peng, Y Cao - Nondestructive Testing and Evaluation, 2024 - Taylor & Francis
Rolling bearings are integral to the operation of various mechanical systems, where their
condition directly impacts equipment reliability and performance. Accurate fault diagnosis is …
condition directly impacts equipment reliability and performance. Accurate fault diagnosis is …
An ensemble deep learning network based on 2D convolutional neural network and 1D LSTM with self-attention for bearing fault diagnosis
L Wang, W Zhao - Applied Soft Computing, 2025 - Elsevier
Intelligent classification methods based on deep learning (DL) have become widely adopted
for bearing fault diagnosis (BFD). However, it is acknowledged that relying on single feature …
for bearing fault diagnosis (BFD). However, it is acknowledged that relying on single feature …
Application of a multi-dimensional synchronous feature mode decomposition for machinery fault diagnosis
Fault diagnosis in complex industrial systems often encounters significant challenges,
including high noise levels, stochastic interference and coupled multi-fault features …
including high noise levels, stochastic interference and coupled multi-fault features …
Cost effective detection of uneven mounting fault in rotary wing drone motors with a CNN based method
Rotary wing drones stand out among Unmanned Aerial Vehicles with their vertical landing
and take-off feature and are used in many industrial applications and different sectors …
and take-off feature and are used in many industrial applications and different sectors …
A novel convolutional neural network with global perception for bearing fault diagnosis
X Li, Y Chen, Y Liu - Engineering Applications of Artificial Intelligence, 2025 - Elsevier
Bearings are key support components in rotating machinery, and their stability is crucial to
the reliability of the entire mechanical system. To address the limitations of existing …
the reliability of the entire mechanical system. To address the limitations of existing …
Fault diagnosis of rotating machinery with high-dimensional imbalance samples based on wavelet random forest
Z Guo, W Du, C Li, X Guo, Z Liu - Measurement, 2025 - Elsevier
Rotary machinery is the key equipment in industrial production, and its running state directly
affects production safety and efficiency. However, in practical applications, rotating …
affects production safety and efficiency. However, in practical applications, rotating …
Inverse Design of FBG-Based Optical Filters Using Deep Learning: A Hybrid CNN-MLP Approach
E Adibnia, M Ghadrdan… - Journal of Lightwave …, 2025 - ieeexplore.ieee.org
Optical filters have always been a critical challenge for advancing communication systems.
Despite significant progress in optical filters, the process of designing, fabricating, and …
Despite significant progress in optical filters, the process of designing, fabricating, and …
Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals
The integration of machine learning algorithms into fault diagnosis is regarded as an
advanced and effective method for detecting electrical system faults. Vibration signals are …
advanced and effective method for detecting electrical system faults. Vibration signals are …