Study on the Optimal Feature Number in the Induction Motor Fault Diagnosis Based on Support Vector Machine Using Current and Vibration Signals
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0106-3_42
Reference4 articles.
1. Javed MR et al (2022) An efficient fault detection method for induction motors using thermal imaging and machine vision. Sustainability 14(15):1–17
2. Mohd Ghazali MH, Rahiman W (2021) Vibration analysis for machine monitoring and diagnosis: a systematic review. Shock Vibration 2021(2):1–25
3. Deng L, Zhang A, Zhao R (2022) Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM. Adv Mech Eng 14(1):1–18
4. Atamuradov V, Medjaher K, Camci F, Zerhouni N, Dersin P, Lamoureux B (2019) Feature selection and fault-severity classification–based machine health assessment methodology for point machine sliding-chair degradation. Qual Reliab Eng 35(4):1–19
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