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A. D. Makwana, G. S. Patange, “Strategic implementation of 5S and its effect on productivity of plastic machinery manufacturing company”. Australian Journal of Mechanical Engineering, 2022, 20(1). pp. 111-120.DOI
A. Heng, S. Zhang, A. C. C. Tan, “Rotating machinery prognostic: state of the art, challenges and opportunities”. Mechanical systems and signal processing, 2009, 23(3), pp. 724-739.DOI
S. Edwards, A. W. Lees, M. I. Friswell, “Fault diagnosis of rotating machinery”. Shock and vibration digest, 1998, 30(1), pp. 4-13.URL
Y. Lei, J. Lin, Z. He, “A review on empirical mode decomposition in fault diagnosis of rotating machinery”. Mechanical systems and signal processing, 2013, 35(1-2), pp. 108-126.DOI
W. Qiao, Z. Li, W. Liu, “Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet”, International Journal of Energy Research, 2022, 46(2), pp. 1766-1788.DOI
M. Grobbelaar, S. Phadikar, E. Ghaderpour, “A Survey on Denoising Techniques of Electro-encephalogram Signals Using Wavelet Transform”. Signals, 2022, 3 (3), pp. 577-586.DOI
M. Zolfaghari, M. R. Golabi, “Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models”. Renewable Energy, 2021, 170, pp. 1367-1381.DOI
S. K. Khare, V. Bajaj, U. R. Acharya, “Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals”. Biocybernetics and Biomedical Engineering, 2021, 41(2), pp. 679-689.DOI
R. Kamgar, R. Tavakoli, P. Rahgozar, “Application of discrete wavelet transform in seismic nonlinear analysis of soil-structure.” Earthquake Spectra, 2021, 37(3), pp. 1980-2012.DOI
J. P. L. Escola, U. B. de Souza, R. C. Guido, “The Haar wavelet transform in IoT digital audio signal processing. Circuits”, Systems, and Signal Processing, 2022, 41(7), pp. 4174-4184.DOI
M. Omidvar, A. Zahedi, H. Bakhshi, “EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers”. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(11), pp. 10395-10403.DOI
Z. Chen, Y. Wang, J. Wu, “Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform”, Applied Intelligence, 2021, 51(8), pp. 5598-5609.DOI
J. Gu, Y. Peng, H. Lu, “Compound fault diagnosis and identification of hoist spindle device based on hilbert huang and energy entropy”. Journal of Mechanical Science and Technology, 2021, 35(10), pp. 4281-4290.DOI
D. Yao, H. Liu, J. Yang, “Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural”, Journal of Intelligent Manufacturing, 2021, 32(3), pp. 729-743.DOI
Q. Wang, Y. Xiao, S. Wang, “A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multiscale Dispersion Entropy”. IEEE Access 2021, 9, pp. 86412-86428.URL
O. H. Abu-Rub, Q. Khan, S. S. Refaat, “Cable Insulation Fault Identification Using Partial Discharge Patterns Analysis”. IEEE Canadian Journal of Electrical and Computer Engineering, 2021, 45(1), pp. 31-41.DOI
A. Mukherjee, P. K. Kundu, A. Das, “Transmission line faults in power system and the different algorithms for identification, classification”. Journal of The Institution of Engineers (India): Series B, 2021, 102(4), pp. 855-877.DOI
X. Zheng, Y. Zeng, M. Zhao, “Early identification and location of short-circuit fault in grid-connected AC microgrid”. IEEE Transactions on Smart Grid, 2021, 12(4), pp. 2869-2878.DOI
S. Z. Hou, W. Guo, Z. Q. Wang, “Deep-Learning-Based Fault Type Identification Using Modified CEEMDAN and Image Augmentation in Distribution Power Grid”. IEEE Sensors Journal, 2021, 22(2), pp. 1583-1596.URL
L. Mbagaya, I. J. K. Kuria, I. J. G. Njiri, “Parameter identification of rolling element bearing system using particle swarm optimisation algorithm: An application to fault diagnostics”. Journal of Sustainable Research in Engineering, 2021, 6(3), pp. 87-99.URL
Pu G, Wang L, Shen J, Dong F. A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology, 2020, 26(2): 146-153.DOI
Vanem E, Brandsæter A. Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine. Journal of Marine Engineering & Technology, 2021, 20(4): 217-234.DOI
Putina A, Rossi D. Online anomaly detection leveraging stream-based clustering and real-time telemetry. IEEE Transactions on Network and Service Management, 2020, 18(1): 839-854.DOI