A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion
Rotating machines is an essential part of any manufacturing industry. The sudden
breakdown of such machines due to improper maintenance can also lead to the industries' …
breakdown of such machines due to improper maintenance can also lead to the industries' …
Bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network
The rapid development of big data leads to many researchers focusing on improving
bearing fault classification accuracy using deep learning models. However, implementing a …
bearing fault classification accuracy using deep learning models. However, implementing a …
Fault feature extraction and diagnosis of rolling bearings based on wavelet thresholding denoising with CEEMDAN energy entropy and PSO-LSSVM
W Chen, J Li, Q Wang, K Han - Measurement, 2021 - Elsevier
In order to improve identification accuracy of rolling bearings with nonlinear and
nonstationary vibration signals, a novel fault diagnosis method based on wavelet …
nonstationary vibration signals, a novel fault diagnosis method based on wavelet …
Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis
J Li, X Yao, X Wang, Q Yu, Y Zhang - Measurement, 2020 - Elsevier
Traditional intelligent fault diagnosis techniques based on artificially selected features fail to
make the most of the raw data information, and are short of the capabilities of feature self …
make the most of the raw data information, and are short of the capabilities of feature self …
Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism
H Wu, J Li, Q Zhang, J Tao, Z Meng - ISA transactions, 2022 - Elsevier
As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize
the adversarial learning of the feature extractor and domain discriminator to extract the …
the adversarial learning of the feature extractor and domain discriminator to extract the …
Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing
Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The
condition of bolt joints has a significant impact on the safe and reliable operation of the …
condition of bolt joints has a significant impact on the safe and reliable operation of the …
High-order synchroextracting transform for characterizing signals with strong AM-FM features and its application in mechanical fault diagnosis
In this paper, a novel time–frequency analysis (TFA) technique termed High-order
Synchroextracting Transform (HSET), is proposed to better characterize the changing …
Synchroextracting Transform (HSET), is proposed to better characterize the changing …
Thermographic image-based diagnosis of failures in electrical motors using deep transfer learning
LFD dos Santos, JL dos Santos Canuto… - … Applications of Artificial …, 2023 - Elsevier
Diagnosing faults in electric motors is a task of great importance for the Industrial Sector
since stopping these types of equipment can cause several invaluable losses for industries …
since stopping these types of equipment can cause several invaluable losses for industries …
Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy
J Li, Z Ye, J Gao, Z Meng, K Tong, S Yu - Applied Soft Computing, 2024 - Elsevier
The current deep learning-based intelligent diagnosis algorithms depend on large amounts
of well-labeled data, but they may not perform well in engineering practice where the fault …
of well-labeled data, but they may not perform well in engineering practice where the fault …