Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning

SMOTE synthetic minority over-sampling technique

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Reducing the dimensionality of data with neural networks

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An efficient k-means clustering algorithm: analysis and implementation

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ADASYN: Adaptive synthetic sampling approach for imbalanced learning

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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

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Exploratory Undersampling for Class-Imbalance Learning

Liu, X., Wu, J., & Zhou, Z. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2007853 · Cited by 2,477 · see citing works

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710 · see citing works

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

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An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data

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MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning

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A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

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Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

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Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals

Ali, J. B., Fnaiech, N., Saïdi, L., Chebel‐Morello, B., & Fnaiech, F. (2014) · Applied Acoustics · doi:10.1016/j.apacoust.2014.08.016 · Cited by 769 · see citing works

A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

Shao, H., Jiang, H., Zhao, H., & Wang, F. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.03.034 · Cited by 686 · see citing works

Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

Soualhi, A., Medjaher, K., & Zerhouni, N. (2014) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2330494 · Cited by 670 · see citing works

2004 IEEE International Joint Conference on Neural Networks

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Rolling bearing fault diagnosis using an optimization deep belief network

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SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

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Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

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Cloud-enabled prognosis for manufacturing

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A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing

Wu, D., Liu, S., Zhang, L., Terpenny, J. P., Gao, R. X., Kurfess, T. R., & Guzzo, J. A. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.02.011 · Cited by 289 · see citing works

Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics

Ocak, H., Loparo, K. A., & Discenzo, F. M. (2007) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2007.01.001 · Cited by 286 · see citing works

MSMOTE: Improving Classification Performance When Training Data is Imbalanced

Hu, S., Liang, Y., Ma, L., & He, Y. (2009) · doi:10.1109/wcse.2009.756 · Cited by 273 · see citing works

Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

Qi, Y., Shen, C., Wang, D., Shi, J., Jiang, X., & Zhu, Z. (2017) · IEEE Access · doi:10.1109/access.2017.2728010 · Cited by 236 · see citing works

Virtualization and deep recognition for system fault classification

Wang, P. E., Ananya, A., Yan, R., & Gao, R. X. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.04.012 · Cited by 163 · see citing works

Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning

Lim, P., Goh, C.-K., & Tan, K. C. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2579658 · Cited by 162 · see citing works

Identifying maximum imbalance in datasets for fault diagnosis of gearboxes

dos Santos, P. T. C., Maudes, J., & Bustillo, A. (2015) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-015-1110-0 · Cited by 75 · see citing works

Current envelope analysis for defect identification and diagnosis in induction motors

Wang, J., Liu, S., Gao, R. X., et al. (2012) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2012.06.005 · Cited by 70 · see citing works

A fast and adaptive bi-dimensional empirical mode decomposition approach for filtering of workpiece surfaces using high definition metrology

Du, S., Liu, T., Huang, D., et al. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.005 · Cited by 39 · see citing works

Using Different Cost Functions to Train Stacked Auto-Encoders

Amaral, T., Silva, L. M., Alexandre, L. A., Kandaswamy, C., Santos, J. M., & de Sá, J. M. (2013) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2013.20 · Cited by 38 · see citing works

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

Bustillo, A., & Rodríguez, J. J. (2013) · International Journal of Systems Science · doi:10.1080/00207721.2013.775378 · Cited by 28 · see citing works

Ensemble classifier selection using multi-objective PSO for fault diagnosis of power transformers

Peimankar, A., Weddell, S. J., Jalal, T. S., et al. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7744248 · Cited by 20 · see citing works

A Novel Data-Driven Fault Diagnosis Method Based on Deep Learning

Zhang, Y., Gao, L., Li, X., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61845-6_44 · Cited by 11 · see citing works

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997 · see citing works

An Oversampling Technique for Classifying Imbalanced Datasets

Nguyen, S. T., Quinn, J., & Olinsky, A. D. (2017) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1477-407020170000012004

Design of Jitter Spectral Shaping as Robust with Various Oversampling Techniques in OFDM

Umar, S. M., Priya, N. G., Gayathri, P., et al. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-981-10-5544-7_63 · Cited by 4 · see citing works

Oversampling And Low Order ΣΔ Modulators

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Investigating the Stability of SMOTE-Based Oversampling on COVID-19 Data

Tan, J. S., Yee, H.-J., Boo, I., et al. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37963-5_33 · Cited by 1 · see citing works

The Jeopardy of Learning from Over-Sampled Class-Imbalanced Medical Datasets

Hassanat, A. B. A., Altarawneh, G., Alkhawaldeh, I. M., Alabdallat, Y. J., Atiya, A. F., Abujaber, A. A., & Tarawneh, A. S. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10218211 · Cited by 14 · see citing works