Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-021-01407-1.pdf
Reference39 articles.
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2. Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Meas J Int Meas Confed 46:1250–1256. https://doi.org/10.1016/j.measurement.2012.11.011
3. Aralikatti SS, Ravikumar KN, Kumar H et al (2020) Comparative study on tool fault diagnosis methods using vibration signals and cutting force signals by machine learning technique. SDHM Struct Durab Heal Monit 14:127–145. https://doi.org/10.32604/SDHM.2020.07595
4. Bordoloi DJ, Tiwari R (2014) Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms. Mech Mach Theory 73:49–60. https://doi.org/10.1016/j.mechmachtheory.2013.10.006
5. Bordoloi DJ, Tiwari R (2017) Identification of suction flow blockages and casing cavitations in centrifugal pumps by optimal support vector machine techniques. J Brazilian Soc Mech Sci Eng 39:2957–2968. https://doi.org/10.1007/s40430-017-0714-z
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