has been cited by the following article(s):
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[1]
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A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis
Materials Today: Proceedings,
2022
DOI:10.1016/j.matpr.2022.07.386
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[2]
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Based on Clustering Algorithm Expert System of Self-learning Rule Base
2022 34th Chinese Control and Decision Conference (CCDC),
2022
DOI:10.1109/CCDC55256.2022.10033463
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[3]
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Based on Clustering Algorithm Expert System of Self-learning Rule Base
2022 34th Chinese Control and Decision Conference (CCDC),
2022
DOI:10.1109/CCDC55256.2022.10033463
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[4]
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A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis
Materials Today: Proceedings,
2022
DOI:10.1016/j.matpr.2022.07.386
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[5]
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Study of Fault Diagnosis for Rolling Bearing Based on Clustering Algorithms
2020 5th International Conference on Control and Robotics Engineering (ICCRE),
2020
DOI:10.1109/ICCRE49379.2020.9096469
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[6]
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Experimental Research on Machinery Fault Simulator (MFS): A Review
2020 Prognostics and Health Management Conference (PHM-Besançon),
2020
DOI:10.1109/PHM-Besancon49106.2020.00019
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[7]
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RT-OPTICS: real-time classification based on OPTICS method to monitor bearings faults
Journal of Intelligent Manufacturing,
2017
DOI:10.1007/s10845-017-1375-6
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[8]
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A Self-Learning Diagnosis Algorithm Based on Data Clustering
Intelligent Control and Automation,
2016
DOI:10.4236/ica.2016.73009
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