Fault Diagnosis of High-Speed Railway Bogies Based on LSTM

Yang, Yang; Qin, Na; Huang, Deqing; Fu, Yuanzhe

doi:10.1109/iccss.2018.8572419

Cited by 8 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LSTM unit is equipped with a 'gate' structure to delete and add information to avoid gradient explosion or gradient disappearance caused by an increasing or decreasing gradient in the backpropagation process [23]. As shown in figure 2, a single LSTM unit has a forget gate, an input gate and an output gate, which can filter, update, and output information, respectively, and every gate structure is made up of multiple hidden neurons.…”

Section: Long Short-term Memorymentioning

confidence: 99%

An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

Chen¹,

Wei²,

Li³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Bearing fault vibration signals collected in real engineering cases often contain environmental noise, which easily causes the fault type characteristics of vibration signals to not be apparent, making it difficult to determine the corresponding fault type when traditional deep learning methods are used for fault diagnosis. To solve the above problems, a neural network model named MCL-DRL was designed, which combines a multiscale wide convolution kernel CNN-LSTM module and a deep residual module for rolling bearing fault diagnosis. In this model, a wide convolution kernel CNN-LSTM structure with different convolution scales is used to extract a variety of different types of frequency and sequential features from vibration signals. It is worth noting that the wide convolution kernel CNN-LSTM structure not only has stronger feature extraction performance compared with the common convolution layer but can also reduce the interference of high-frequency noise. Moreover, the deep residual module with a wide convolution kernel CNN-LSTM structure is used to further improve the feature expression ability of the proposed model. The above algorithm enables the proposed model to better extract the fault features hidden in the noise signal.When compared with some state-of-the-art methods, the experimental results showed that this model has better antinoise performance and better generalization ability for rolling bearing fault diagnosis.

show abstract

Section: Long Short-term Memorymentioning

confidence: 99%

An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

Chen¹,

Wei²,

Li³

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In the previous study, a failure diagnostic approach was used to analyze the drone's vibration and predict the abnormality using deep learning to detect the abnormal state of the drone [18][19][20]. Furthermore, studies are conducted to diagnose abnormalities by analyzing and predicting the vibration of a motor using LSTM [21][22][23][24][25][26][27][28][29] and GRU [30][31][32][33][34][35]. Moreover, Table 1 represents the papers using more than two RNN techniques in terms of purpose, method, and evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

Vibration Prediction of Flying IoT Based on LSTM and GRU

Hong

2022

Electronics

View full text Add to dashboard Cite

Drones, flying Internet of Things (IoT), have been widely used in several industrial fields, including rescue, delivery, military, and agriculture. Motors connected to a drone’s propellers play a crucial role in its movement. However, once the motor is damaged, the drone is at risk of falling. Thus, to prevent the drone from falling, an accurate and reliable prediction of motor vibration is necessary. In this study, four types of time series vibration data collected in the time domain from motors are predicted using long short-term memory (LSTM) and gated recurrent unit (GRU), and the accuracy and time efficiency of the predicted and actual vibration waveforms are compared and examined. According to the simulation results, the coefficient of determination, R2, and the root mean square error values obtained from the actual and predicted vibrations by the LSTM and GRU are similar. Furthermore, both the LSTM and GRU show excellent performance in forecasting future motor vibration, but GRU can predict future vibration about 22.79% faster than LSTM.

show abstract

“…Therefore, it is essential to conduct studies on predicting vibrations to prevent drone crashes. Previous studies have been conducted to predict time series vibration data using various recurrent neural network (RNN) techniques: long short-term memory (LSTM) [8][9][10][11][12][13][14][15][16], attention-LSTM (Attn.-LSTM) [17,18], bidirectional-LSTM (Bi-LSTM) [19][20][21][22], gated recurrent unit (GRU) [23][24][25][26][27], attention-GRU (Attn.-GRU) [28], and bidirectional GRU (Bi-GRU) [29][30][31]. Table 1 below the coefficient of determination used to evaluate the accuracy of the predicted vibrations.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Analysis of Vibration Prediction Using RNN Techniques

Lee

Hong

2022

Electronics

View full text Add to dashboard Cite

Drones are increasingly used in several industries, including rescue, firefighting, and agriculture. If the motor connected to a drone’s propeller is damaged, there is a risk of a drone crash. Therefore, to prevent such incidents, an accurate and quick prediction tool of the motor vibrations in drones is required. In this study, normal and abnormal vibration data were collected from the motor connected to the propeller of a drone. The period and amplitude of the vibrations are consistent in normal vibrations, whereas they are irregular in abnormal vibrations. The collected vibration data were used to train six recurrent neural network (RNN) techniques: long short-term memory (LSTM), attention-LSTM (Attn.-LSTM), bidirectional-LSTM (Bi-LSTM), gated recurrent unit (GRU), attention-GRU (Attn.-GRU), and bidirectional GRU (Bi-GRU). Then, the simulation runtime it took for each RNN technique to predict the vibrations and the accuracy of the predicted vibrations were analyzed to compare the performances of the RNN model. Based on the simulation results, the Attn.-LSTM and Attn.-GRU techniques, incorporating the attention mechanism, had the best efficiency compared to the conventional LSTM and GRU techniques, respectively. The attention mechanism calculates the similarity between the input value and the to-be-predicted value in advance and reflects the similarity in the prediction.

show abstract

Fault Diagnosis of High-Speed Railway Bogies Based on LSTM

Cited by 8 publications

References 7 publications

An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model

Vibration Prediction of Flying IoT Based on LSTM and GRU

Comparative Performance Analysis of Vibration Prediction Using RNN Techniques

Contact Info

Product

Resources

About