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Linear maximum margin tensor classification based on flexible convex hulls for fault diagnosis of rolling bearings

He, Z., Cheng, J., Li, J., & Yang, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.024 · Cited by 31 · see citing works

Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

Vernekar, K., Kumar, H., & Gangadharan, K. V. (2017) · Sadhana · doi:10.1007/s12046-017-0678-9 · Cited by 31 · see citing works

Few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization

Zhang, D., Zheng, K. W., Bai, Y., Yao, D., Yang, D., & Wang, S. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac8303 · Cited by 30 · see citing works

SELECTION OF DISCRETE WAVELETS FOR FAULT DIAGNOSIS OF MONOBLOCK CENTRIFUGAL PUMP USING THE J48 ALGORITHM

Muralidharan, V., & Sugumaran, V. (2013) · Applied Artificial Intelligence · doi:10.1080/08839514.2012.721694 · Cited by 30 · see citing works

Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution

Hou, D., Zhang, J., Yang, Z., Liu, S., Huang, P., & Zhang, G. (2015) · Optics Express · doi:10.1364/oe.23.017487 · Cited by 29 · see citing works

Improving the rotary kiln-electric furnace process for ferronickel production: Data analytics-based assessment of dust insufflation into the rotary kiln flame

Romero, J. M., Pardo, Y., Parra, M., Castillo, A. D. J., Maury, H., Corredor, L., Sánchez, I., Rueda, B. S., & González‐Quiroga, A. (2021) · Alexandria Engineering Journal · doi:10.1016/j.aej.2021.08.036 · Cited by 28 · see citing works

Life assessment and health monitoring of rolling element bearings: an experimental study

Sharma, A., Amarnath, M., & Kankar, P. K. (2018) · Life Cycle Reliability and Safety Engineering · doi:10.1007/s41872-018-0044-x · Cited by 28 · see citing works

Development of a soldering quality classifier system using a hybrid data mining approach

Tsai, T. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.11.097 · Cited by 28 · see citing works

Enhancing random forest classification with NLP in DAMEH: A system for DAta Management in eHealth Domain

Amato, F., Coppolino, L., Cozzolino, G., Mazzeo, G., Moscato, F., & Nardone, R. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.08.091 · Cited by 27 · see citing works

Multidomain variance-learnable prototypical network for few-shot diagnosis of novel faults

Long, J., Chen, Y., Huang, H., Yang, Z., Huang, Y., & Li, C. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02123-2 · Cited by 27 · see citing works

A dual sensor signal fusion approach for detection of faults in rotating machines

Saimurugan, M., & Ramprasad, R. (2017) · Journal of Vibration and Control · doi:10.1177/1077546316689644 · Cited by 26 · see citing works

Fault diagnosis of bearings through vibration signal using Bayes classifiers

Kumar, H., Kumar, T. A. R., Amarnath, M., & Sugumaran, V. (2013) · International Journal of Computer Aided Engineering and Technology · doi:10.1504/ijcaet.2014.058002 · Cited by 26 · see citing works

Decision Trees and Random Forests

Nandi, A. K., & Ahmed, H. (2019) · doi:10.1002/9781119544678.ch10 · Cited by 26 · see citing works

Engine gearbox fault diagnosis using machine learning approach

Vernekar, K., Kumar, H., & Gangadharan, K. V. (2018) · Journal of Quality in Maintenance Engineering · doi:10.1108/jqme-11-2015-0058 · Cited by 25 · see citing works

Applying improved multi-scale entropy and support vector machines for bearing health condition identification

Zhang, L., Xiong, G., Liu, H., Zou, H., & Guo, W. (2009) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1243/09544062jmes1784 · Cited by 24 · see citing works

A comparative analysis of classical and one class SVM classifiers for machine fault detection using vibration signals

Amir, R. B., Gul, S. T., & Khan, A. Q. (2016) · International Conference on Emerging Technologies · doi:10.1109/icet.2016.7813262 · Cited by 24 · see citing works

Research on bearing fault diagnosis method based on cjbm with semi-supervised and imbalanced data

Li, S., Peng, Y., Bin, G., Shen, Y., Guo, Y. Z., Li, B., Jiang, Y., & Fan, C. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-10073-4 · Cited by 23 · see citing works

Wise-local response convolutional neural network based on Naïve Bayes theorem for rotating machinery fault classification

Aljemely, A. H., Xuan, J., Xu, L., Jawad, F. K. J., & Al-Azzawi, O. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02252-2 · Cited by 24 · see citing works

ROTOR FAULT DIAGNOSIS BASED ON PCA AND DECISION TREE

Wu, X. (2007) · Zhendong yu chongji · Cited by 1 · see citing works

Research on the Method of Rotary Machinery Fault Diagnosis based on PCA and DBN

Li, H., Qi, Z. L., Hu, J., & Zhang, X. Y. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1043/2/022044 · Cited by 3 · see citing works

Principal component analysis approach for detecting faults in rotary machines based on vibrational and electrical fused data

Elsamanty, M. M., Ibrahim, A., & Salman, W. S. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110559 · Cited by 53 · see citing works

Fault monitoring and classification of rotating machine based on PCA and KNN

Yinghua, Y., Shi, G., & Xiang, S. (2018) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2018.8407418 · Cited by 13 · see citing works

Rotary Machines Fault Diagnosis based on Principal Component Analysis

Elsamanty, M. M., Salman, W. S., & Ibrahim, A. (2021) · Engineering Research Journal · doi:10.21608/erj.2021.193822 · Cited by 3 · see citing works