Data segmentation and augmentation methods based on raw data using deep neural networks approach for rotating machinery fault diagnosis
Z Meng, X Guo, Z Pan, D Sun, S Liu - IEEE Access, 2019 - ieeexplore.ieee.org
Intelligent fault diagnosis has been widely used for mechanical fault diagnosis. Most
intelligent diagnostic methods extract fault features from the frequency domain or other …
intelligent diagnostic methods extract fault features from the frequency domain or other …
Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning
Fault diagnosis is important for the maintenance of rotating machinery. The detection of
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …
faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this …
An intelligent deep feature learning method with improved activation functions for machine fault diagnosis
Rotating machinery has been developed with high complexity and precision, and bearings
and gears are crucial components in the machinery system. Deep learning has attracted …
and gears are crucial components in the machinery system. Deep learning has attracted …
A new local-global deep neural network and its application in rotating machinery fault diagnosis
X Zhao, M Jia - Neurocomputing, 2019 - Elsevier
Currently, it is a great challenge to effectively acquire more widespread equipment health
information for guaranteeing safe production and timely fault maintenance in the process of …
information for guaranteeing safe production and timely fault maintenance in the process of …
Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …
Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery
Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an
accurate and timely diagnosis method is necessary. With the breakthrough in deep learning …
accurate and timely diagnosis method is necessary. With the breakthrough in deep learning …
A lighted deep convolutional neural network based fault diagnosis of rotating machinery
S Ma, W Cai, W Liu, Z Shang, G Liu - Sensors, 2019 - mdpi.com
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-
resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of …
resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of …
Average descent rate singular value decomposition and two-dimensional residual neural network for fault diagnosis of rotating machinery
H Liang, J Cao, X Zhao - IEEE Transactions on Instrumentation …, 2022 - ieeexplore.ieee.org
Fault diagnosis of rotating machinery is difficult under the strong noisy environment.
Although singular value decomposition (SVD) can remove noise from vibration signals, the …
Although singular value decomposition (SVD) can remove noise from vibration signals, the …
Fault diagnosis of rotating machinery based on deep reinforcement learning and reciprocal of smoothness index
Rotating machinery are widely used in industry, and vibration analysis is one of the most
common methods to monitor health condition of rotating machinery. However, due to the …
common methods to monitor health condition of rotating machinery. However, due to the …
Fault diagnosis of rotating machinery based on recurrent neural networks
Fault diagnosis of rotating machinery is essential for maintaining system performance and
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and …
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