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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period
2020
Journal of Intelligent & Fuzzy Systems
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Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term ...
The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance ...
A fault diag-364 nosis model of wind turbine rolling bearing based on 365 wavelet packet transform and long-term memory NN 366 is established. ...
doi:10.3233/jifs-189301
fatcat:eeg2rr7y7bblponbnnfxfrhabu
Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism
2021
Shock and Vibration
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Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network ...
the type of rolling bearing fault. ...
GRU and long short-term memory network (LSTM) [29] [30] [31] are solving the problem of gradient disappearance in RNN. ey can consider the longterm and short-term dependence in time series more completely ...
doi:10.1155/2021/6660243
doaj:42507a9836244a1880eecf8f9c7973ad
fatcat:ihruvsiesjapdgl2kqyal3ypnm
A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults
2020
Sensors
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to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. ...
We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data ...
The Long-Short Term Memory (LSTM) recurrent neural network architecture [23] (shown in Figure 2 ) overcomes some of the difficulties of training ordinary RNNs by introducing both a memory cell and a ...
doi:10.3390/s20185112
pmid:32911771
fatcat:u354a5wkhnetrntegzmjx33zlm
End-to-End CNN+LSTM Deep Learning Approach for Bearing Fault Diagnosis
[article]
2020
arXiv
pre-print
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We used equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest ...
Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train/test datasets ...
We could also notice some state-of-the-art articles like [13] and [14] which have simultaneously paid attention to CNN and Long-Short-Term-Memory (LSTM) networks for bearing fault diagnosis. ...
arXiv:1909.07801v5
fatcat:gjbiazgki5dr7pijdct6bjecxe
Deep Learning Approaches to Aircraft Maintenance, Repair and Overhaul: A Review
2018
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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Although deep learning in general is not yet largely exploited for aircraft health, from our search, we identified four main architectures employed to MRO, namely, Deep Autoencoders, Long Short-Term Memory ...
, Convolutional Neural Networks and Deep Belief Networks. ...
Yuan et al. [15]
Long Short-Term Memory
Fault diagnosis and remaining useful life estimation of
aero-engine
ElSaid et al. [16]
Long Short-Term Memory + Ant Colony Optimization [17]
Prognosis of excess ...
doi:10.1109/itsc.2018.8569502
dblp:conf/itsc/RengasamyMF18
fatcat:fdetlamp6vesngmpu7icccvqxe
A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools
2018
Inventions
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health diagnosis applications [101], a DL method for signal recognition and the diagnosis of spacecraft [102], long short-term memory neural network (LSTMNN) for fault diagnosis and estimation of the ...
Inventions 2018, 3, 41 2 of 28 state automata [4], the continuous time RNN approach to dynamical systems [5], the RNN scheme for long short-term memory (LSTM) [6,7], the echo state network (ESN) approach ...
Hochreiter and Schmidhuber [6] addressed a novel, efficient, gradient-based method called long short-term memory (LSTM) to solve the complex and artificial long time-lag tasks. ...
doi:10.3390/inventions3030041
fatcat:6qrwhmrl2bfwrgmovqvsyx5p3y
A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM
2020
Sensors
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Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. ...
This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. ...
[22] constructed a long short-term memory (LSTM) network to predict the RUL of mechanical equipment and verified the advantages of LSTM in RUL prediction by using C-MAPSS datasets. Zhao et al. ...
doi:10.3390/s20071864
pmid:32230874
fatcat:jvrwf7h5djdw3ez54rpb3cqigm
Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis
2021
Entropy
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This paper suggests a new method to predict the Remaining Useful Life (RUL) of rolling bearings based on Long Short Term Memory (LSTM), in order to obtain the degradation condition of the rolling bearings ...
The rationale of this work is to combine the two methods—the density-based spatial clustering of applications with noise and LSTM—to identify the abnormal state in rolling bearings, then estimate the RUL ...
End
Long Short-Term Memory Long short-term memory (LSTM) is a type of artificial recurrent neural network (RNN) [30] used in the field of deep learning. ...
doi:10.3390/e23070791
fatcat:zemneyy6njaw7piqr2j6uxcfo4
Fault Diagnosis for Bearing Based on 1DCNN and LSTM
2021
Shock and Vibration
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Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. ...
In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies ...
Long Short-Term Memory Network. Recurrent neural network (RNN) is a kind of deep neural network [24] . ...
doi:10.1155/2021/1221462
fatcat:im5h25wbunb5nhjilhq6rnk4tq
An End-to-End Intelligent Fault Diagnosis Application for Rolling Bearings Based on MobileNet
2021
IEEE Access
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INDEX TERMS Fault diagnosis, rolling bearing, deep neural network, mobilenet. ...
Recently, existing studies have shown that most of fault diagnoses are implemented by using deep neural network. ...
In addition, a convolutional bidirectional long short term memory network was designed based on afore model, where the convolution network was used to extract local features of time series, and the bidirectional ...
doi:10.1109/access.2021.3065195
fatcat:y6ptm77z3bf5hljjxhlv2s2o5a
Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments
2021
Applied Sciences
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In this paper, we propose a denoising autoencoder (DAE) and multi-scale convolution recurrent neural network (MS-CRNN), wherein the DAE accurately inspects bearing defects in the same environment as bearing ...
Most equipment failures occur in rotating equipment, with bearing damage being the biggest cause of failure in rotating equipment. ...
Long Short-Term Memory (LSTM) Using an RNN, one can effectively model time series data [39, 40] . ...
doi:10.3390/app11093963
doaj:0b64fcf264484680b7e90b1e3256a12c
fatcat:rscshqu4j5aixiz3bxazrpaycm
Table of Contents
2021
IEEE Transactions on Industrial Informatics
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(Contents Continued from Front Cover) Variational Autoencoder Bidirectional Long and Short-Term Memory Neural Network Soft-Sensor Model Based on Batch Training Strategy . . . . . . . . . . . . . . . . ...
Hossain 5572 Recurrent Neural Network Model for IoT and Networking Malware Threat Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tii.2021.3074236
fatcat:ecg3x7fxpjhctcugcpysidoy2u
Combined Remaining Life Prediction of Multiple Bearings Based on EEMD-BILSTM
2022
Symmetry
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) and Bi-directional Long Short-Term Memory (BiLSTM) is proposed. ...
bearing full-life experiments, and the effectiveness of the method is verified by comparing the prediction results with several main recurrent neural networks. ...
Principle of Bi-Directional Long and Short-Term Memory Network LSTM, as a particular case of RNN, not only avoids gradient explosion and disappearance, but also has a larger memory capacity than RNN, which ...
doi:10.3390/sym14020251
fatcat:fobmru34nnc5dog6zn5skrndfu
Fault Diagnosis of Motor Bearings Based on a Convolutional Long Short-Term Memory Network of Bayesian Optimization
2021
IEEE Access
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In [16] , convolutional neural networks with two convolution kernels of different sizes and long short-term memory networks were used to directly input the original signals for bearing fault diagnosis ...
. 2) Combined with the convolutional layer of a convolutional neural network and a long short-term memory network, a motor bearing fault diagnosis model is established. ...
doi:10.1109/access.2021.3093363
fatcat:wmtu22j5ivewjggpoqjvap2dxe
Application of Rotating Machinery Fault Diagnosis Based on Deep Learning
2021
Shock and Vibration
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networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault ...
After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural ...
Researchers have developed improved models such as long short-term memory models (LSTM) and gated recurrent units (GRU) based on standard RNN to solve the shortcomings of RNN. ...
doi:10.1155/2021/3083190
fatcat:4no2xr3f75hszivh7uhq3r2t6y
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