Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine

This paper presents a fault diagnosis scheme for induction machines (IMs) using Support Vector Machine (SVM) and Random Forests (RFs). First, a number of time domain and frequency-domain features are extracted from vibration and current signals in different operating conditions of IM. Then, these features are combined and considered as the input of SVM-based classification model. To avoid overfitting, RF is utilized to determine the most dominant features contributing to accurate classification. It is proved that the proposed method is capable of achieving highly accurate fault diagnosis results for broken rotor bar and eccentricity faults and it can appropriately handle the high dimensionality of the combined data.

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Fault diagnosis, Induction motors, Machine learning, Multible signal classification, Support vector machine

DOI

10.1109/icem49940.2020.9270689

Citation

Nemat Saberi, A, Sandirasegaram, S, Belahcen, A, Vaimann, T & Sobra, J 2020, Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine . in Proceedings of the International Conference on Electrical Machines, ICEM 2020 ., 9270689, Proceedings (International Conference on Electrical Machines), IEEE, International Conference on Electrical Machines, Virtual, Online, 23/08/2020 . https://doi.org/10.1109/icem49940.2020.9270689

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https://urn.fi/URN:NBN:fi:aalto-2020123160232

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[article-cris] Sähkötekniikan korkeakoulu / ELEC

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Multi-Sensor Fault Diagnosis of Induction Motors Using Random Forests and Support Vector Machine

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