Mobile QR Code QR CODE

2024

Acceptance Ratio

21%

REFERENCES

1 
G.~Sharma, K.~Umapathy, and S.~Krishnan, ``Trends in audio signal feature extraction methods,'' Applied Acoustics, vol.~158, 107020, 2020.DOI
2 
E. G. Plaza, P. N. López, and E. B. González, ``Efficiency of vibration signal feature extraction for surface finish monitoring in CNC machining,'' Journal of Manufacturing Processes, vol. 44, pp. 145-157, 2019.DOI
3 
G. He, K. Ding, and H. Lin, ``Fault feature extraction of rolling element bearings using sparse representation,'' Journal of Sound and Vibration, vol. 366, pp. 514-527, 2016.DOI
4 
R. Xiao, Q. Hu, and J. Li, ``Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine,'' Measurement, vol. 146, pp. 479-489, 2019.DOI
5 
Z. Ren, S. Zhou, E. Chunhui, M. Gong, B. Li, and B. Wen, ``Crack fault diagnosis of rotor systems using wavelet transforms,'' Computers & Electrical Engineering, vol. 45, pp. 33-41, 2015.DOI
6 
B. Dolenc, P. Boškoski, and Đ. Juričić, ``Distributed bearing fault diagnosis based on vibration analysis,'' Mechanical Systems and Signal Processing, vol. 66, pp. 521-532, 2016.DOI
7 
A. S. Wessam, Y. Li, and P. Wen, ``K-complexes detection in EEG signals using fractal and frequency features coupled with an ensemble classification model,'' Neuroscience, vol. 422, pp. 119-133, 2019.DOI
8 
S. Rukhsar, Y. U. Khan, O. Farooq, M. Sarfraz, and A. T. Khan, ``Patient-specific epileptic seizure prediction in long-term scalp EEG signal using multivariate statistical process control,'' IRBM, vol. 40, no. 6, pp. 320-331, 2019.DOI
9 
Y. Yang, W. Yang, and D. Jiang, ``Simulation and experimental analysis of rolling element bearing fault in rotor-bearing-casing system,'' Engineering Failure Analysis, vol. 92, pp. 205-221, 2018.DOI
10 
D. Zhao, T. Wang, R. X. Gao, and F. Chu, ``Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction,'' Mechanical Systems and Signal Processing, vol. 134, 106297, 2019.DOI
11 
C. Wu, P. Jiang, C. Ding, F. Feng, and T. Chen, ``Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network,'' Computers in Industry, vol. 108, pp. 53-61, 2019.DOI
12 
J. Zhang, S. Yi, G. Guo, H. Gao, X. Hong, and H. Song, ``A new bearing fault diagnosis method based on modified convolutional neural networks,'' Chinese Journal of Aeronautics, vol. 33, no. 2, pp. 439-447, 2020.DOI
13 
C. Li, D. Zhao, S. Mu, W. Zhang, N. Shi, and L. Li, ``Fault diagnosis for distillation process based on CNN–DAE,'' Chinese Journal of Chemical Engineering, vol. 27, no. 3, pp. 598-604, 2019.DOI
14 
S. Wang, J. Xiang, Y. Zhong, and Y. Zhou, ``Convolutional neural network-based hidden Markov models for rolling element bearing fault identification,'' Knowledge-Based Systems, vol. 144, pp. 65-76, 2018.DOI
15 
C. Lu, Z. Wang, and B. Zhou, ``Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification,'' Advanced Engineering Informatics, vol. 32, pp. 139-151, 2017.DOI
16 
Q. An, Z. Tao, X. Xu, M. El Mansori, and M. Chen, ``A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network,'' Measurement, vol. 154, 107461, 2020.DOI
17 
V. Pandiyan and T. Tjahjowidodo, ``Use of acoustic emissions to detect change in contact mechanisms caused by tool wear in abrasive belt grinding process,'' Wear, vol. 436, p. 203047, 2019.DOI
18 
M. T. García-Ordás, E. Alegre-Gutiérrez, R. Alaiz-Rodríguez, and V. González-Castro, ``Tool wear monitoring using an online, automatic and low cost system based on local texture,'' Mechanical Systems and Signal Processing, vol. 112, pp. 98-112, 2018.DOI
19 
R. Srinivasan, V. Jacob, A. Muniappan, S. Madhu, and M. Sreenevasulu, ``Modeling of surface roughness in abrasive water jet machining of AZ91 magnesium alloy using fuzzy logic and regression analysis,'' Materials Today: Proceedings, vol. 22, pp. 1059-1064, 2020.DOI
20 
A. K. Parida and K. Maity, ``Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM),'' Measurement, vol. 137, pp. 375-381, 2019.DOI
21 
H. Y. Chen and C. H. Lee, ``Deep learning approach for vibration signals applications,'' Sensors, vol. 21, no. 11, 3929, 2021.DOI
22 
Y. Zhao, Z. H. Guo, and J. M. Yan, ``Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks,'' Journal of Vibroengineering, vol. 19, no. 4, pp. 2456-2474, 2017.DOI
23 
H. Hu, B. Tang, X. Gong, W. Wei, and H. Wang, ``Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks,'' IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2106-2116, 2017.DOI
24 
H. Luo, L. Bo, C. Peng, and D. Hou, ``Fault diagnosis for high-speed train axle-box bearing using simplified shallow information fusion convolutional neural network,'' Sensors, vol. 20, no. 17, 4930, 2020.DOI
25 
Y. Zou, Y. Zhang, and H. Mao, ``Fault diagnosis on the bearing of traction motor in high-speed trains based on deep learning,'' Alexandria Engineering Journal, vol. 60, no. 1, pp. 1209-1219, 2021.DOI
26 
Z. Jin, D. Chen, D. He, Y. Sun, and X. Yin, ``Bearing fault diagnosis based on VMD and improved CNN,'' Journal of Failure Analysis and Prevention, vol. 23, no. 1, pp. 165-175, 2023.DOI
27 
K. Liang, N. Qin, D. Huang, and Y. Fu, ``Convolutional recurrent neural network for fault diagnosis of high-speed train bogie,'' Complexity, vol. 2018, no. 1, 4501952, 2018.DOI